diff --git a/.gitattributes b/.gitattributes index c1860cbd09d04b89411f5dd05b8e149ca822b187..93fd06870b8d8fed4af5ef10f584332b1da9d22f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -68,3 +68,7 @@ Helios/_DEV3/demo_data/Vidprom_filtered_extended.txt filter=lfs diff=lfs merge=l Helios/checkpoints/Helios-Base/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text Helios/checkpoints/Helios-Distilled/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text Helios/checkpoints/Helios-Mid/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text +CT/lung/paper.pdf filter=lfs diff=lfs merge=lfs -text +CT/lung2/参考论文.pdf filter=lfs diff=lfs merge=lfs -text +CT/liver/logs/download_msd_liver.log filter=lfs diff=lfs merge=lfs -text +CT/liver/logs/download_waw_tace.log filter=lfs diff=lfs merge=lfs -text diff --git a/CT/liver/configs/benchmark/densenet121_ct_only.yaml b/CT/liver/configs/benchmark/densenet121_ct_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c284327f89304793285b90af719591c38c83102 --- /dev/null +++ b/CT/liver/configs/benchmark/densenet121_ct_only.yaml @@ -0,0 +1,50 @@ +stage: waw_deep_outcome +root: . +seed: 2026 +device: auto +amp: true +epochs: 60 +output_dir: experiments/benchmark/densenet121_ct_only/checkpoints +result_dir: experiments/benchmark/densenet121_ct_only + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.9 + survival_bins_days: [180, 365, 540, 730, 1095] + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + +model: + architecture: densenet121 + in_channels: 4 + feature_dim: 256 + clinical_dim: 0 + num_response_classes: 3 + survival_bins: 5 + dropout: 0.2 + +optimizer: + lr: 0.0002 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_response: 1.0 + lambda_progression: 1.0 + lambda_survival: 0.5 + lambda_brier: 0.05 + response_class_weights: [1.0, 3.5, 0.6] + progression_pos_weight: 3.0 + +selection: + metrics: + progression_auc: 0.45 + response_acc: 0.25 + pfs_cindex: 0.15 + os_cindex: 0.15 diff --git a/CT/liver/configs/benchmark/full_multitask_clean.yaml b/CT/liver/configs/benchmark/full_multitask_clean.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fe2dd5d1e63d321f84799ef06651a797573a9379 --- /dev/null +++ b/CT/liver/configs/benchmark/full_multitask_clean.yaml @@ -0,0 +1,86 @@ +stage: waw_multitask +root: . +seed: 2026 +device: auto +amp: true +epochs: 80 +output_dir: experiments/benchmark/full_multitask/checkpoints +result_dir: experiments/benchmark/full_multitask + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.9 + survival_bins_days: [180, 365, 540, 730, 1095] + clinical_csv: data/processed/waw_tace/clinical.csv + clinical_columns: + - age + - gender_woman + - etiology_mixed + - etiology_HCV + - etiology_HBV + - etiology_alcoholic + - etiology_NASH + - etiology_cryptogenic + - lesions_number + - lesion1_localisation + - lesion1_diameter + - lesion1_LIRADS + - lesion2_localisation + - lesion2_diameter + - lesion2_LIRADS + - lesion3_localisation + - lesion3_diameter + - lesion3_LIRADS + - biopsy + - lab_albumin + - lab_creatinine + - lab_bilirubin + - lab_afp + - lab_inr + - lab_alt + - cps + - bclc + - hap_score + - mhap_2 + - albi_tae + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + +model: + init_filters: 32 + dropout: 0.2 + num_seg_classes: 3 + num_response_classes: 3 + survival_bins: 5 + pretrained_checkpoint: experiments/checkpoints/msd_liver_seg/best.pt + +optimizer: + lr: 0.0002 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_seg: 1.0 + lambda_cls: 1.0 + lambda_surv: 0.5 + lambda_util: 0.2 + lambda_cal: 0.05 + seg_class_weights: [0.1, 1.0, 8.0] + response_class_weights: [1.0, 3.5, 0.6] + progression_pos_weight: 3.0 + seg_include_background: true + seg_lambda_dice: 1.0 + seg_lambda_ce: 1.0 + +selection: + metrics: + dice_class_1: 0.2 + dice_class_2: 0.45 + progression_auc: 0.25 + response_acc: 0.1 diff --git a/CT/liver/configs/benchmark/full_multitask_improved.yaml b/CT/liver/configs/benchmark/full_multitask_improved.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b0791f6e0676ae6ff7f5af01251a8013e4e85914 --- /dev/null +++ b/CT/liver/configs/benchmark/full_multitask_improved.yaml @@ -0,0 +1,101 @@ +stage: waw_multitask +root: . +seed: 2026 +device: auto +amp: true +epochs: 80 +output_dir: experiments/benchmark/full_multitask_improved/checkpoints +result_dir: experiments/benchmark/full_multitask_improved + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.95 + tumor_focus_sampling: true + small_tumor_voxel_threshold: 10000 + small_tumor_weight: 2.5 + hard_cases_csv: experiments/benchmark/full_multitask/case_metrics.csv + num_hard_cases: 24 + hard_case_weight: 3.0 + phase_dropout_prob: 0.15 + single_phase_prob: 0.20 + survival_bins_days: [180, 365, 540, 730, 1095] + clinical_csv: data/processed/waw_tace/clinical.csv + clinical_columns: + - age + - gender_woman + - etiology_mixed + - etiology_HCV + - etiology_HBV + - etiology_alcoholic + - etiology_NASH + - etiology_cryptogenic + - lesions_number + - lesion1_localisation + - lesion1_diameter + - lesion1_LIRADS + - lesion2_localisation + - lesion2_diameter + - lesion2_LIRADS + - lesion3_localisation + - lesion3_diameter + - lesion3_LIRADS + - biopsy + - lab_albumin + - lab_creatinine + - lab_bilirubin + - lab_afp + - lab_inr + - lab_alt + - cps + - bclc + - hap_score + - mhap_2 + - albi_tae + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + weighted_sampling: true + +model: + init_filters: 32 + dropout: 0.2 + num_seg_classes: 3 + num_response_classes: 3 + survival_bins: 5 + pretrained_checkpoint: experiments/checkpoints/msd_liver_seg/best.pt + +optimizer: + lr: 0.0002 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_seg: 1.0 + lambda_cls: 1.15 + lambda_surv: 0.6 + lambda_util: 0.04 + lambda_cal: 0.05 + utility_balance_weight: 0.5 + seg_class_weights: [0.05, 1.0, 12.0] + response_class_weights: [1.0, 3.5, 0.6] + progression_pos_weight: 3.0 + seg_include_background: true + seg_lambda_dice: 1.0 + seg_lambda_ce: 0.7 + seg_tversky_alpha: 0.25 + seg_tversky_beta: 0.75 + seg_lambda_tversky: 0.6 + seg_focal_gamma: 2.0 + seg_lambda_focal: 0.25 + +selection: + metrics: + dice_class_1: 0.15 + dice_class_2: 0.55 + progression_auc: 0.2 + response_acc: 0.1 diff --git a/CT/liver/configs/benchmark/full_multitask_improved_v2.yaml b/CT/liver/configs/benchmark/full_multitask_improved_v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ba03a27ee0ae8763d96d86a894e2aef0af648d61 --- /dev/null +++ b/CT/liver/configs/benchmark/full_multitask_improved_v2.yaml @@ -0,0 +1,101 @@ +stage: waw_multitask +root: . +seed: 2026 +device: auto +amp: true +epochs: 80 +output_dir: experiments/benchmark/full_multitask_improved_v2/checkpoints +result_dir: experiments/benchmark/full_multitask_improved_v2 + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.9 + tumor_focus_sampling: true + small_tumor_voxel_threshold: 10000 + small_tumor_weight: 1.5 + hard_cases_csv: experiments/benchmark/full_multitask/case_metrics.csv + num_hard_cases: 16 + hard_case_weight: 1.5 + phase_dropout_prob: 0.05 + single_phase_prob: 0.05 + survival_bins_days: [180, 365, 540, 730, 1095] + clinical_csv: data/processed/waw_tace/clinical.csv + clinical_columns: + - age + - gender_woman + - etiology_mixed + - etiology_HCV + - etiology_HBV + - etiology_alcoholic + - etiology_NASH + - etiology_cryptogenic + - lesions_number + - lesion1_localisation + - lesion1_diameter + - lesion1_LIRADS + - lesion2_localisation + - lesion2_diameter + - lesion2_LIRADS + - lesion3_localisation + - lesion3_diameter + - lesion3_LIRADS + - biopsy + - lab_albumin + - lab_creatinine + - lab_bilirubin + - lab_afp + - lab_inr + - lab_alt + - cps + - bclc + - hap_score + - mhap_2 + - albi_tae + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + weighted_sampling: true + +model: + init_filters: 32 + dropout: 0.2 + num_seg_classes: 3 + num_response_classes: 3 + survival_bins: 5 + pretrained_checkpoint: experiments/benchmark/full_multitask/checkpoints/best.pt + +optimizer: + lr: 0.00008 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_seg: 1.0 + lambda_cls: 1.0 + lambda_surv: 0.5 + lambda_util: 0.02 + lambda_cal: 0.05 + utility_balance_weight: 0.25 + seg_class_weights: [0.1, 1.0, 8.0] + response_class_weights: [1.0, 3.5, 0.6] + progression_pos_weight: 3.0 + seg_include_background: true + seg_lambda_dice: 1.0 + seg_lambda_ce: 0.8 + seg_tversky_alpha: 0.3 + seg_tversky_beta: 0.7 + seg_lambda_tversky: 0.25 + seg_focal_gamma: 2.0 + seg_lambda_focal: 0.10 + +selection: + metrics: + dice_class_1: 0.15 + dice_class_2: 0.55 + progression_auc: 0.2 + response_acc: 0.1 diff --git a/CT/liver/configs/benchmark/resnet18_ct_only.yaml b/CT/liver/configs/benchmark/resnet18_ct_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7216d0307f701cec6986c4e014450fb3357c4669 --- /dev/null +++ b/CT/liver/configs/benchmark/resnet18_ct_only.yaml @@ -0,0 +1,50 @@ +stage: waw_deep_outcome +root: . +seed: 2026 +device: auto +amp: true +epochs: 60 +output_dir: experiments/benchmark/resnet18_ct_only/checkpoints +result_dir: experiments/benchmark/resnet18_ct_only + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.9 + survival_bins_days: [180, 365, 540, 730, 1095] + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + +model: + architecture: resnet18 + in_channels: 4 + feature_dim: 256 + clinical_dim: 0 + num_response_classes: 3 + survival_bins: 5 + dropout: 0.2 + +optimizer: + lr: 0.0002 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_response: 1.0 + lambda_progression: 1.0 + lambda_survival: 0.5 + lambda_brier: 0.05 + response_class_weights: [1.0, 3.5, 0.6] + progression_pos_weight: 3.0 + +selection: + metrics: + progression_auc: 0.45 + response_acc: 0.25 + pfs_cindex: 0.15 + os_cindex: 0.15 diff --git a/CT/liver/configs/benchmark/waw_seg_only.yaml b/CT/liver/configs/benchmark/waw_seg_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..70560c2e16ded0cd4889a396bfe911b3aff35972 --- /dev/null +++ b/CT/liver/configs/benchmark/waw_seg_only.yaml @@ -0,0 +1,50 @@ +stage: waw_multitask +root: . +seed: 2026 +device: auto +amp: true +epochs: 60 +output_dir: experiments/benchmark/segresnet_seg_only/checkpoints +result_dir: experiments/benchmark/segresnet_seg_only + +data: + manifest: manifests/waw_tace_training_manifest.csv + val_fraction: 0.2 + stratify_column: label_progression + patch_shape: [64, 96, 96] + foreground_prob: 0.9 + survival_bins_days: [180, 365, 540, 730, 1095] + +loader: + batch_size: 1 + num_workers: 2 + persistent_workers: false + +model: + init_filters: 32 + dropout: 0.2 + num_seg_classes: 3 + num_response_classes: 3 + survival_bins: 5 + pretrained_checkpoint: experiments/checkpoints/msd_liver_seg/best.pt + +optimizer: + lr: 0.0002 + weight_decay: 0.0001 + eta_min: 0.000001 + +loss: + lambda_seg: 1.0 + lambda_cls: 0.0 + lambda_surv: 0.0 + lambda_util: 0.0 + lambda_cal: 0.0 + seg_class_weights: [0.1, 1.0, 8.0] + seg_include_background: true + seg_lambda_dice: 1.0 + seg_lambda_ce: 1.0 + +selection: + metrics: + dice_class_1: 0.25 + dice_class_2: 0.75 diff --git 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new file mode 100644 index 0000000000000000000000000000000000000000..80451f21a5cf06ff896b4a64aa70f2cbf53bfa15 --- /dev/null +++ b/CT/liver/manifests/preprocessing_summary.csv @@ -0,0 +1,4 @@ +dataset,processed_rows,notes +msd_liver,131,MSD training image/label symlinks and manifest +waw_tace,233,Patient-level multiphase CT manifest with clinical/outcome columns +hcc_tace_seg,680,DICOM series-level manifest; phase is inferred from descriptions diff --git a/CT/liver/manifests/waw_tace_manifest.csv b/CT/liver/manifests/waw_tace_manifest.csv new file mode 100644 index 0000000000000000000000000000000000000000..f9546f418e0694114bb8ed70f479ac35ec983cff --- /dev/null +++ b/CT/liver/manifests/waw_tace_manifest.csv @@ -0,0 +1,234 @@ 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b/CT/liver/paper/manuscript_draft.md @@ -0,0 +1,372 @@ +# Cohort-aware Multiphase CT-first Modeling for TACE Response Characterization and Prognosis in Hepatocellular Carcinoma + +> 当前版本：论文初稿 v0.1 +> 日期：2026-05-27 +> 说明：这是基于当前代码、数据和 benchmark 结果写出的学习型初稿。部分消融实验和 outcome/survival 优化仍未完成，文中已用“当前结果/待补充”标注，避免把工程中还没有完成的实验写成最终结论。 + +## Abstract + +Transarterial chemoembolization (TACE) is an important locoregional therapy for hepatocellular carcinoma (HCC), but post-treatment response and prognosis remain difficult to predict from heterogeneous clinical and imaging evidence. In this work, we develop a cohort-aware multiphase CT-first framework for joint liver/tumor segmentation, TACE response characterization, progression prediction, and survival risk estimation. The model uses multiphase contrast-enhanced CT as the primary input, produces liver/tumor support maps through a 3D SegResNet backbone, incorporates phase availability and phase utility estimation, and fuses structured clinical variables with explicit missingness masks for patient-level prediction. + +We organize three public cohorts into a unified experimental protocol. MSD/LiTS liver tumor data are used for segmentation pretraining, WAW-TACE is used for model development and internal validation, and HCC-TACE-Seg is used for external segmentation validation. On WAW-TACE internal validation, the current full multitask model achieves liver Dice 0.917, tumor Dice 0.645, and mean foreground Dice 0.781, outperforming segmentation-only SegResNet and recent segmentation baselines. On HCC-TACE-Seg external validation, the model obtains liver Dice 0.758, tumor Dice 0.570, and mean foreground Dice 0.664, outperforming the evaluated SegResNet-only and recent segmentation alternatives. These results suggest that anatomical support maps and multitask learning improve segmentation transfer, while outcome and survival prediction still require additional validation, cross-validation, and ablation. + +## 1. Introduction + +Hepatocellular carcinoma (HCC) is commonly treated with transarterial chemoembolization (TACE), especially in patients who are not immediate candidates for curative resection or transplantation. After TACE, clinicians need to answer several related questions: where are the liver and tumor regions, whether the tumor responds to therapy, whether the patient is likely to progress, and what long-term prognosis may be expected. These questions are naturally linked, but they are often modeled separately in existing computational pipelines. + +From a computer vision perspective, this problem is not only a 3D segmentation task. It combines at least four learning problems: + +1. **3D semantic segmentation**: segment liver and tumor voxels from CT. +2. **Treatment response classification**: predict response categories after TACE. +3. **Progression/risk prediction**: predict whether progression occurs. +4. **Survival analysis**: estimate patient-level PFS, OS, and TTP risk. + +The task is further complicated by the structure of HCC imaging data. TACE patients are usually scanned with multiphase contrast-enhanced CT, including non-contrast/native, arterial, portal venous, and delayed phases. Different phases may emphasize different biological and anatomical information. For example, arterial enhancement can highlight hypervascular HCC lesions, while portal venous phase may better define liver parenchyma and lesion boundaries. Therefore, a model that simply treats CT as a single volume may miss the structured value of multiphase imaging. + +Another practical challenge is data heterogeneity. Public HCC/TACE datasets rarely provide all labels simultaneously. Some datasets contain segmentation masks but no treatment outcome; others contain clinical labels and survival information but are smaller; external validation datasets may contain only single-phase CT and masks. This makes a conventional single-task, single-cohort training protocol insufficient for a paper-level study. + +To address these issues, this project builds a **cohort-aware multiphase CT-first multitask framework**. The key idea is to use CT as the default evidence source, use liver/tumor segmentation as anatomical support, add phase utility estimation for multiphase CT, and fuse clinical variables only when available. The framework is designed to be compatible with heterogeneous supervision: segmentation labels, response labels, progression labels, and survival labels can be used together when available, while external segmentation-only datasets can still be used for independent validation. + +The current work makes the following contributions: + +1. **Unified HCC-TACE benchmark construction.** We organize MSD/LiTS, WAW-TACE, and HCC-TACE-Seg into a shared preprocessing, training, and evaluation protocol. +2. **Multiphase CT-first multitask model.** We implement a 3D SegResNet-based model that jointly outputs liver/tumor masks, response logits, progression risk, survival hazards, and phase utility scores. +3. **External validation on HCC-TACE-Seg.** We evaluate the proposed model and multiple recent segmentation baselines on an external HCC-TACE-Seg cohort. +4. **Benchmark-oriented comparison.** We compare clinical-only, radiomics-based, CT-only, segmentation-only, recent segmentation, and full multitask models under a consistent internal validation protocol. + +At the current stage, the segmentation evidence is stronger than the outcome/survival evidence. The internal and external segmentation results support the value of the multitask model. However, the current clean benchmark shows that CT-only ResNet remains competitive for some survival endpoints, so the outcome and survival components still need further optimization and ablation before being written as final claims. + +## 2. Related Work + +### 2.1 Liver and Tumor Segmentation in 3D CT + +Liver and tumor segmentation is a central task in abdominal CT analysis. Earlier deep learning approaches often use 3D U-Net-like encoder-decoder architectures. MONAI SegResNet follows this family and provides a practical 3D residual segmentation backbone. nnU-Net later became a standard strong baseline for biomedical segmentation because it self-configures preprocessing, network structure, and training settings for a given dataset. + +Recent segmentation methods introduce Transformer or state-space style modules for long-range modeling. TransUNet combines convolutional feature extraction with Transformer representations. SegMamba, Swin-UMamba, and HybridMamba explore Mamba-like sequence modeling, local-global mixing, or hybrid frequency/spatial context for volumetric medical segmentation. In our current benchmark, these methods are treated as segmentation baselines: they predict liver/tumor masks but do not directly predict TACE response or survival. + +For this project, segmentation has two roles. First, it provides a standard measurable task using Dice, IoU, ASSD, and NSD. Second, it acts as an anatomical support map for downstream outcome prediction. This is important because a response/prognosis model should ideally focus on tumor and liver context rather than arbitrary whole-volume signals. + +### 2.2 Foundation Models and Promptable Medical Segmentation + +Medical segmentation foundation models such as VISTA3D, SAM-Med3D, Medical SAM 2, and MedSAM2 aim to generalize across organs, modalities, and datasets. These models are relevant because they can potentially provide zero-shot or prompt-assisted liver/tumor masks. However, most promptable segmentation models are designed for mask prediction, not for TACE response, progression, or survival prediction. + +In this project, foundation segmentation models are considered as possible external segmentation baselines or mask generators. They should not be treated as direct competitors for the full multitask TACE model unless they are extended with outcome heads. The current automatic VISTA3D/NV-Segment-CT benchmark is not yet valid because complete prediction files are missing, so it is listed as a future comparison rather than an active result. + +### 2.3 Radiomics and Clinical Models for TACE Outcome Prediction + +Traditional TACE outcome studies frequently use radiomics and clinical variables. A typical pipeline segments the tumor, extracts handcrafted shape/intensity/texture features, selects a subset of features, and trains a machine learning classifier such as SVM, random forest, or logistic regression. Clinical nomograms similarly combine variables such as age, AFP, liver function, tumor burden, BCLC stage, or Child-Pugh-related factors. + +These methods remain important baselines because they reflect the standard modeling style in many medical papers. They also help answer whether a deep CT model truly adds value beyond clinical or radiomics features. In our current WAW-TACE clean benchmark, clinical-only and radiomics-only baselines are weak or unstable, while CT-only ResNet performs strongly for some progression/survival metrics. This means that deep CT features contain useful prognostic information, but the multitask fusion model still needs stronger outcome optimization. + +### 2.4 Multitask and Survival Modeling in Medical Imaging + +Multitask learning is useful when related tasks share anatomical or biological structure. In this project, segmentation, response prediction, progression prediction, and survival prediction are connected through tumor burden and liver context. A shared 3D backbone can learn features useful for both mask prediction and patient-level risk estimation. + +Survival prediction differs from ordinary classification because it uses time-to-event labels and censoring. The current implementation uses discrete-time hazard heads for PFS, OS, and TTP. The model outputs hazard logits over predefined time bins, which are converted into risk scores for C-index evaluation. This part is implemented, but the current clean benchmark suggests that survival performance is not yet the strongest component of the model. + +## 3. Method + +### 3.1 Problem Definition + +For each WAW-TACE patient, the input consists of multiphase CT volumes: + +```text +X = {X_native, X_arterial, X_portal, X_delayed} +``` + +The target annotations may include: + +```text +Y_seg: liver/tumor voxel labels +Y_response: TACE response class +Y_progression: binary progression label +Y_survival: PFS / OS / TTP time-to-event labels +C: structured clinical variables +M_phase: phase availability mask +M_clinical: clinical missingness mask +``` + +The model is trained to predict: + +```text +P_seg = liver/tumor segmentation probabilities +P_response = response class probabilities +P_progression = progression probability +H_pfs, H_os, H_ttp = discrete-time survival hazard logits +U_phase = phase utility scores +``` + +### 3.2 Data Cohorts + +The current project uses three public data sources with different roles: + +| Dataset | Role | Used for Training | Used for Validation | Labels Used | +|---|---|---:|---:|---| +| MSD/LiTS liver tumor data | segmentation pretraining | yes | yes | liver/tumor masks | +| WAW-TACE | main model development | yes | yes | multiphase CT, masks, clinical variables, response/progression/survival | +| HCC-TACE-Seg | external validation | no | yes | CT and liver/tumor masks | + +The WAW-TACE clean benchmark uses 233 cases split into 187 training cases and 46 internal validation cases. The split uses `seed=2026`, `val_fraction=0.2`, and stratification by `label_progression`. The HCC-TACE-Seg external set contains 105 cases and is used only for segmentation validation. + +### 3.3 Preprocessing + +All datasets are converted into NPZ-based training caches. CT intensities are normalized after liver-window-oriented clipping. Volumes and labels are cropped or padded to a fixed patch size: + +```text +patch_shape = [64, 96, 96] +``` + +For WAW-TACE, the four phases are stored as four input channels: + +```text +channel 0: native / non-contrast CT +channel 1: arterial phase +channel 2: portal venous phase +channel 3: delayed phase +``` + +Missing phases are zero-filled, and phase availability is passed as an explicit mask. Clinical variables are read from the clean clinical table, normalized using training-set statistics, and concatenated with a missingness mask. + +For HCC-TACE-Seg external validation, the dataset contains single-phase CT. Because the WAW-trained models expect four channels, the current evaluation uses: + +```text +repeat_1ch_to_4ch +``` + +That is, the same HCC CT volume is copied into all four channels. This keeps the trained checkpoint unchanged but should be interpreted as an external adaptation protocol rather than true multiphase evaluation. + +### 3.4 Network Architecture + +The current full model uses a MONAI SegResNet-style 3D backbone. The same encoder supports segmentation and patient-level prediction. + +```text +Multiphase CT volumes + | + v +Shared 3D SegResNet encoder + | + +--> Segmentation decoder --> liver/tumor masks + | + +--> pooled image features + | + +--> phase statistics and phase utility + | + +--> clinical encoder with missingness mask + | + v +Fusion MLP + | + +--> response head + +--> progression head + +--> PFS hazard head + +--> OS hazard head + +--> TTP hazard head +``` + +The segmentation decoder produces three classes: + +```text +0: background +1: liver +2: tumor +``` + +The phase utility module receives pooled backbone features and the phase availability mask. It outputs a soft utility distribution over available CT phases: + +```text +U_phase = softmax(f_phase(z, M_phase)) +``` + +The clinical encoder receives: + +```text +[clinical_values, clinical_missingness_mask] +``` + +This design prevents the model from confusing a true zero-valued clinical feature with a missing value. + +### 3.5 Loss Function + +The training objective combines segmentation, classification, survival, utility, and calibration losses: + +```text +L = λ_seg L_seg + + λ_cls L_cls + + λ_surv L_surv + + λ_util L_util + + λ_cal L_cal +``` + +Current clean benchmark weights are: + +```text +λ_seg = 1.0 +λ_cls = 1.0 +λ_surv = 0.5 +λ_util = 0.2 +λ_cal = 0.05 +``` + +Segmentation uses Dice + cross entropy with class weights. Response uses multiclass cross entropy. Progression uses binary classification loss. PFS, OS, and TTP are modeled as discrete-time survival tasks over fixed time bins. + +### 3.6 Compared Methods + +The current benchmark includes four groups of methods: + +| Group | Methods | Purpose | +|---|---|---| +| Clinical/tabular baselines | clinical logistic, radiomics SVM, radiomics + clinical RF | test whether simple clinical/radiomics features are enough | +| CT-only outcome baselines | 3D ResNet18, 3D DenseNet121 | test outcome prediction from CT without segmentation multitask learning | +| Segmentation baselines | SegResNet seg-only, nnU-Net ResEnc, TransUNet, SegMamba, Swin-UMamba, HybridMamba | compare liver/tumor segmentation ability | +| Proposed model | full multitask model | jointly segment, fuse clinical variables, estimate response/progression/survival | + +## 4. Experiments + +### 4.1 Experimental Setup + +The main internal benchmark is performed on WAW-TACE: + +| Split | Cases | Progression 0 | Progression 1 | +|---|---:|---:|---:| +| Train | 187 | 142 | 45 | +| Validation | 46 | 35 | 11 | + +The external benchmark is performed on HCC-TACE-Seg: + +| Dataset | Cases | Use | +|---|---:|---| +| HCC-TACE-Seg | 105 | external liver/tumor segmentation validation | + +The main evaluation metrics are: + +| Task | Metrics | +|---|---| +| Segmentation | Dice, IoU, ASSD, NSD | +| Response classification | accuracy, balanced accuracy, macro-F1 | +| Progression prediction | AUC, AP, Brier, ECE, balanced accuracy | +| Survival prediction | PFS/OS/TTP C-index | + +### 4.2 WAW-TACE Internal Validation + +The current WAW-TACE clean benchmark is summarized below. + +| Method | Task Type | Liver Dice | Tumor Dice | Mean FG Dice | Response Acc | Progression AUC | PFS C-index | OS C-index | TTP C-index | +|---|---|---:|---:|---:|---:|---:|---:|---:|---:| +| Clinical-only logistic | tabular outcome | - | - | - | 0.478 | 0.582 | 0.473 | 0.710 | 0.473 | +| Radiomics SVM | radiomics outcome | - | - | - | 0.348 | 0.499 | 0.389 | 0.636 | 0.389 | +| Radiomics + clinical RF | radiomics + clinical | - | - | - | 0.609 | 0.496 | 0.284 | 0.582 | 0.284 | +| 3D ResNet18 CT-only | deep CT outcome | - | - | - | 0.587 | 0.706 | 0.821 | 0.565 | 0.816 | +| 3D DenseNet121 CT-only | deep CT outcome | - | - | - | 0.587 | 0.630 | 0.800 | 0.491 | 0.711 | +| SegResNet seg-only | segmentation | 0.910 | 0.631 | 0.771 | - | - | - | - | - | +| Full multitask, ours | multitask | 0.917 | 0.645 | 0.781 | 0.630 | 0.696 | 0.389 | 0.663 | 0.379 | + +The full multitask model currently achieves the best internal segmentation performance among the main benchmark methods, with mean foreground Dice 0.781 and tumor Dice 0.645. However, outcome and survival results are mixed. In particular, the CT-only ResNet18 baseline shows stronger PFS and TTP C-index than the current full multitask model. Therefore, the current result supports the segmentation and anatomical support-map part of the method more strongly than the survival modeling part. + +### 4.3 Recent Segmentation Baselines on WAW-TACE + +We also evaluate recent segmentation-oriented methods on the same WAW-TACE internal split. + +| Method | Venue/Source | Parameters | Liver Dice | Tumor Dice | Mean FG Dice | Mean FG IoU | +|---|---|---:|---:|---:|---:|---:| +| nnU-Net ResEnc 2024 | MICCAI 2024 | 17.42M | 0.913 | 0.590 | 0.751 | 0.663 | +| TransUNet 2024 | Medical Image Analysis 2024 | 6.71M | 0.904 | 0.560 | 0.732 | 0.642 | +| SegMamba 2024 | MICCAI 2024 | 8.27M | 0.918 | 0.623 | 0.771 | 0.678 | +| Swin-UMamba 2024 | MICCAI 2024 | 8.22M | 0.916 | 0.605 | 0.760 | 0.670 | +| HybridMamba 2025 | MICCAI 2025 | 8.63M | 0.919 | 0.563 | 0.741 | 0.654 | +| Full multitask, ours | this work | 18.92M | 0.917 | 0.645 | 0.781 | 0.688 | + +Among recent segmentation baselines, SegMamba performs best on mean foreground Dice and is close to the SegResNet-only baseline. The proposed full multitask model achieves the highest tumor Dice and mean foreground Dice in this internal comparison. + +### 4.4 HCC-TACE-Seg External Validation + +HCC-TACE-Seg is used as an external segmentation validation dataset. Because it lacks WAW-style outcome and survival labels in the current engineering setup, only segmentation metrics are reported. + +| Method | Liver Dice | Tumor Dice | Mean FG Dice | Mean FG IoU | Liver NSD | Tumor NSD | +|---|---:|---:|---:|---:|---:|---:| +| Full multitask, ours | 0.758 | 0.570 | 0.664 | 0.546 | 0.662 | 0.578 | +| SegResNet seg-only | 0.731 | 0.475 | 0.603 | 0.490 | 0.618 | 0.486 | +| SegMamba 2024 | 0.725 | 0.261 | 0.493 | 0.389 | 0.645 | 0.251 | +| HybridMamba 2025 | 0.720 | 0.265 | 0.492 | 0.389 | 0.619 | 0.268 | +| Swin-UMamba 2024 | 0.700 | 0.245 | 0.472 | 0.370 | 0.638 | 0.242 | +| TransUNet 2024 | 0.596 | 0.290 | 0.443 | 0.338 | 0.594 | 0.291 | +| nnU-Net ResEnc 2024 | 0.612 | 0.223 | 0.417 | 0.318 | 0.617 | 0.228 | + +The proposed full multitask model obtains the best external segmentation performance, with tumor Dice 0.570 and mean foreground Dice 0.664. This suggests that multitask training may improve the transferability of liver/tumor representation, even though the model was trained on WAW-TACE and evaluated on an external HCC-TACE-Seg cohort. + +However, the external result is still lower than WAW internal validation: + +| Test Set | Liver Dice | Tumor Dice | Mean FG Dice | Interpretation | +|---|---:|---:|---:|---| +| WAW-TACE internal validation | 0.917 | 0.645 | 0.781 | same-cohort internal split | +| HCC-TACE-Seg external validation | 0.758 | 0.570 | 0.664 | cross-cohort external validation | + +This gap indicates domain shift between WAW-TACE and HCC-TACE-Seg. Possible causes include different CT acquisition protocols, phase availability, annotation style, tumor size distribution, and the single-phase-to-four-channel adaptation used for external evaluation. + +### 4.5 Phase Ablation + +The project also includes a phase ablation experiment. This experiment was run using an older multitask configuration, so it should be interpreted as a phase-analysis result rather than the final fair benchmark. + +| Phase Mode | Mean FG Dice | Tumor Dice | Progression AUC | Response Acc | +|---|---:|---:|---:|---:| +| all phases | 0.772 | 0.618 | 1.000 | 0.957 | +| native only | 0.391 | 0.030 | 1.000 | 0.957 | +| arterial only | 0.595 | 0.347 | 1.000 | 0.935 | +| portal only | 0.678 | 0.460 | 1.000 | 0.957 | +| delayed only | 0.614 | 0.402 | 1.000 | 0.957 | +| utility top1 | 0.435 | 0.062 | 1.000 | 0.957 | + +The main observation is that all-phase input is best for segmentation, while portal phase is the strongest single phase. The current `utility_top1` mode is weak for segmentation, suggesting that the learned phase utility may be more aligned with outcome signals than with segmentation quality. A future version should separate segmentation-oriented phase utility from outcome-oriented phase utility. + +### 4.6 Current Limitations + +The current work is not yet a final submission-ready paper. Important limitations remain: + +1. **Outcome and survival performance are not yet strong enough.** The full multitask model does not currently outperform CT-only ResNet18 on PFS/TTP C-index. +2. **Full ablation is incomplete.** Only phase ablation has been run. Planned ablations such as removing clinical variables, missingness masks, survival heads, support maps, and pretraining still need to be completed. +3. **External validation only covers segmentation.** HCC-TACE-Seg currently supports external mask evaluation but not response/progression/survival evaluation. +4. **Single-phase external adaptation is imperfect.** HCC-TACE-Seg CT is repeated into four channels, which is practical but not equivalent to true multiphase CT. +5. **Recent Mamba baselines are engineering reproductions/adapters.** The current environment does not use official `mamba_ssm` kernels or official pretrained weights, so these should be described as repository-native reproductions rather than exact official implementations. + +## 5. Discussion + +The current evidence suggests that a CT-first multitask model is promising for HCC-TACE analysis, especially for segmentation and external transfer. Compared with segmentation-only SegResNet, the full multitask model improves tumor Dice on both WAW internal validation and HCC external validation. This is an important signal: even when outcome heads are not yet optimal, multitask supervision may encourage the backbone to learn tumor representations that transfer better. + +At the same time, the clean benchmark is honest about the current weakness. CT-only ResNet18 currently performs better than the full multitask model on PFS and TTP C-index. This means the final paper should not claim that the current model already solves prognosis prediction. Instead, the next engineering step should focus on survival head design, loss balancing, risk calibration, and multi-seed validation. If survival remains weak, the paper can still be positioned around segmentation-supported response/progression modeling, but the survival claim should be reduced. + +The external validation result is useful for the paper narrative. The model was trained on WAW-TACE and evaluated on HCC-TACE-Seg without fine-tuning, yet it outperformed all evaluated baselines on external tumor segmentation. This supports a plausible claim that anatomical multitask representation improves cross-cohort segmentation robustness. The domain gap remains visible, so future work should explore domain adaptation, external fine-tuning, or stronger CT pretraining. + +## 6. Conclusion + +We present a cohort-aware multiphase CT-first framework for HCC-TACE modeling. The framework unifies segmentation, response characterization, progression prediction, and survival estimation in a shared 3D model with liver/tumor support maps, phase utility estimation, and clinical fusion. Current experiments show strong internal and external segmentation results, with the full multitask model achieving the best tumor Dice among evaluated methods on both WAW-TACE internal validation and HCC-TACE-Seg external validation. Outcome and survival prediction remain under active development and require additional ablation, calibration, and validation before final clinical claims can be made. + +## References To Fill + +The final paper should add formal BibTeX references for at least: + +1. WAW-TACE dataset, Radiology: Artificial Intelligence 2024. +2. MSD Task03 Liver / LiTS liver tumor segmentation datasets. +3. SegResNet / autoencoder-regularized 3D U-Net. +4. nnU-Net, Nature Methods 2021. +5. nnU-Net Revisited, MICCAI 2024. +6. TransUNet, Medical Image Analysis 2024. +7. SegMamba, MICCAI 2024. +8. Swin-UMamba, MICCAI 2024. +9. HybridMamba, MICCAI 2025. +10. HCC/TACE radiomics and clinical nomogram papers used as outcome baselines. + +## Current File Map + +Key project artifacts used by this draft: + +| Artifact | Path | +|---|---| +| Main plan | `plan.md` | +| Benchmark notes | `对比方法Benchmark.md` | +| WAW benchmark summary | `experiments/benchmark/benchmark_summary.csv` | +| Recent segmentation benchmark | `experiments/recent_benchmark/benchmark_summary.csv` | +| HCC external benchmark | `experiments/hcc_external_benchmark/benchmark_summary.csv` | +| Main multitask config | `configs/benchmark/full_multitask_clean.yaml` | +| HCC external benchmark config | `configs/hcc_external_benchmark.yaml` | +| Main model code | `src/liver_tace/model.py` | +| HCC external benchmark code | `src/liver_tace/hcc_external_benchmark.py` | + diff --git a/CT/liver/paper/manuscript_draft_zh.md b/CT/liver/paper/manuscript_draft_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..90e4563eaa8f10739237bb70b3d581cd599cccdb --- /dev/null +++ b/CT/liver/paper/manuscript_draft_zh.md @@ -0,0 +1,372 @@ +# 面向肝细胞癌 TACE 疗效表征与预后评估的队列感知多期 CT-first 建模 + +> 当前版本：中文论文初稿 v0.1 +> 日期：2026-05-27 +> 说明：本文是 `manuscript_draft.md` 的中文版本，目的是帮助理解当前工作的研究问题、方法设计和实验结果。部分消融实验和 outcome/survival 优化仍未完成，文中已明确标注，避免把尚未完成的实验写成最终结论。 + +## 摘要 + +经动脉化疗栓塞术（transarterial chemoembolization, TACE）是肝细胞癌（hepatocellular carcinoma, HCC）的重要局部治疗方式之一。然而，TACE 后疗效反应和长期预后仍然难以仅凭单一影像或临床指标准确预测。本文提出一个队列感知的多期增强 CT-first 框架，用于联合完成肝脏/肿瘤分割、TACE 疗效表征、进展风险预测和生存风险估计。模型以多期增强 CT 为核心输入，通过 3D SegResNet backbone 生成 liver/tumor support maps，引入期相可用性和 phase utility 建模，并融合结构化临床变量及其缺失标记，实现患者级预测。 + +我们将三个公开队列组织进统一实验协议：MSD/LiTS 肝脏肿瘤数据用于分割预训练，WAW-TACE 用于主模型开发和内部验证，HCC-TACE-Seg 用于外部分割验证。在 WAW-TACE 内部验证集上，当前完整多任务模型取得 liver Dice 0.917、tumor Dice 0.645、mean foreground Dice 0.781，优于 segmentation-only SegResNet 和近两年分割基线。在 HCC-TACE-Seg 外部验证集上，模型取得 liver Dice 0.758、tumor Dice 0.570、mean foreground Dice 0.664，同样优于已评估的 SegResNet-only 和近期分割方法。这些结果说明，解剖支撑图和多任务学习可能提升分割表征的跨队列泛化能力；但 outcome 和 survival 预测仍需要进一步的交叉验证、消融实验和优化。 + +## 1. Introduction + +肝细胞癌患者常接受 TACE 治疗，尤其是在不适合立即接受手术切除或肝移植的患者中。TACE 后，临床上通常需要回答几个相互关联的问题：肝脏和肿瘤区域在哪里，肿瘤是否对治疗有反应，患者是否容易发生进展，以及长期预后风险如何。这些问题在医学上相互关联，但在很多计算方法中往往被拆成彼此独立的任务。 + +从计算机视觉角度看，这个问题并不只是一个 3D segmentation 任务，而是至少包含四类学习问题： + +1. **3D semantic segmentation**：从 CT 中分割 liver 和 tumor voxel。 +2. **Treatment response classification**：预测 TACE 后疗效反应类别。 +3. **Progression/risk prediction**：预测患者是否发生进展。 +4. **Survival analysis**：估计患者级 PFS、OS 和 TTP 风险。 + +HCC 影像数据的结构进一步增加了建模难度。TACE 患者通常有多期增强 CT，包括 native/non-contrast、arterial、portal venous 和 delayed phases。不同期相强调的生物学和解剖信息不同。例如，动脉期可以突出 HCC 的富血供增强特征，而门静脉期可能更有利于肝实质和病灶边界识别。因此，如果把 CT 简化成单个 volume，模型可能无法利用多期增强 CT 的结构化信息。 + +另一个实际挑战是数据异质性。公开 HCC/TACE 数据集很少同时具备所有标签。一些数据集有 segmentation mask，但没有治疗结局；一些数据集有临床和随访信息，但规模较小；外部验证数据集可能只有单期 CT 和 mask。这使得传统的单任务、单队列训练协议不足以支撑完整论文。 + +为了解决这些问题，本项目构建了一个 **cohort-aware multiphase CT-first multitask framework**。核心思想是：以 CT 作为默认证据来源，以 liver/tumor segmentation 作为解剖支撑，引入 phase utility 来建模多期 CT 的贡献，并在临床变量可用时进行融合。该框架能够兼容异质监督：当 segmentation、response、progression 和 survival 标签可用时联合训练；当外部数据集只有 mask 时，也可以单独用于分割泛化验证。 + +当前工作的主要贡献包括： + +1. **构建统一的 HCC-TACE benchmark。** 将 MSD/LiTS、WAW-TACE 和 HCC-TACE-Seg 组织到共享的数据预处理、训练和评估流程中。 +2. **提出多期 CT-first 多任务模型。** 实现一个基于 3D SegResNet 的模型，联合输出 liver/tumor mask、response logits、progression risk、survival hazards 和 phase utility scores。 +3. **完成 HCC-TACE-Seg 外部验证。** 在外部 HCC-TACE-Seg 队列上评估本文模型和多个近期 segmentation baseline。 +4. **建立 benchmark-oriented comparison。** 在一致内部验证协议下比较 clinical-only、radiomics-based、CT-only、segmentation-only、recent segmentation 和 full multitask models。 + +当前阶段，segmentation 证据强于 outcome/survival 证据。内部和外部分割结果支持多任务模型的有效性；但 clean benchmark 显示，CT-only ResNet 在部分 survival endpoint 上仍然有竞争力。因此，outcome 和 survival 分支还需要进一步优化和消融，不能在现阶段过度声称。 + +## 2. Related Work + +### 2.1 3D CT 中的肝脏与肿瘤分割 + +肝脏和肿瘤分割是腹部 CT 分析的核心任务。早期深度学习方法通常采用 3D U-Net 类 encoder-decoder 架构。MONAI SegResNet 属于这类实用的 3D residual segmentation backbone。nnU-Net 后来成为医学图像分割中的强基线，因为它能够针对具体数据集自动配置预处理、网络结构和训练策略。 + +近年的分割方法进一步引入 Transformer 或状态空间模型来建模长程依赖。TransUNet 结合卷积特征提取和 Transformer 表征；SegMamba、Swin-UMamba 和 HybridMamba 探索 Mamba-like sequence modeling、local-global mixing 或 hybrid frequency/spatial context。在当前 benchmark 中，这些方法被作为 segmentation baseline：它们预测 liver/tumor mask，但不直接预测 TACE response 或 survival。 + +在本项目中，segmentation 有两重作用。第一，它是一个可标准评估的任务，可用 Dice、IoU、ASSD 和 NSD 衡量。第二，它作为后续 outcome prediction 的 anatomical support map。对于疗效和预后模型来说，关注 tumor 和 liver context 比直接从整图中黑箱分类更合理。 + +### 2.2 Foundation Models 与 Promptable Medical Segmentation + +VISTA3D、SAM-Med3D、Medical SAM 2 和 MedSAM2 等医学分割 foundation models 试图跨器官、跨模态、跨数据集泛化。这些模型有潜力提供 zero-shot 或 prompt-assisted liver/tumor mask。不过，大多数 promptable segmentation models 主要面向 mask 预测，而不是 TACE response、progression 或 survival 预测。 + +因此，在本项目中，foundation segmentation models 更适合作为外部分割基线或 mask generator。除非额外接 outcome heads，否则它们不应被视为完整多任务 TACE 模型的直接竞争者。当前 VISTA3D/NV-Segment-CT 自动分割 benchmark 还没有有效结果，因为完整 prediction files 缺失，所以暂时列为未来对比而不是当前有效实验。 + +### 2.3 TACE 结局预测中的 Radiomics 与 Clinical Models + +传统 TACE outcome 研究大量使用 radiomics 和临床变量。典型流程是先分割肿瘤，然后提取 handcrafted shape/intensity/texture features，再进行特征选择，并训练 SVM、random forest 或 logistic regression 等机器学习分类器。临床 nomogram 则常常融合年龄、AFP、肝功能、肿瘤负荷、BCLC stage 或 Child-Pugh 相关指标。 + +这些方法仍然是重要 baseline，因为它们代表很多医学论文中的常规建模方式，也能回答深度 CT 模型是否真的超过 clinical/radiomics features。当前 WAW-TACE clean benchmark 中，clinical-only 和 radiomics-only baseline 整体较弱或不稳定；而 CT-only ResNet 在部分 progression/survival 指标上较强。这说明 deep CT features 包含有用的预后信息，但当前 multitask fusion model 仍需要更强的 outcome 优化。 + +### 2.4 医学影像中的 Multitask 与 Survival Modeling + +当多个任务共享解剖或生物学结构时，多任务学习通常有价值。本项目中，segmentation、response prediction、progression prediction 和 survival prediction 都与 tumor burden 和 liver context 相关。共享 3D backbone 可以同时学习对 mask prediction 和 patient-level risk estimation 有用的特征。 + +Survival prediction 不同于普通分类，因为它包含 time-to-event labels 和 censoring。当前实现采用 discrete-time hazard heads 来预测 PFS、OS 和 TTP。模型在预定义时间 bins 上输出 hazard logits，并将其转换成 risk score，用 C-index 评估。该部分已经实现，但当前 clean benchmark 表明 survival performance 还不是模型最强部分。 + +## 3. Method + +### 3.1 问题定义 + +对每个 WAW-TACE 患者，输入是多期 CT volumes： + +```text +X = {X_native, X_arterial, X_portal, X_delayed} +``` + +可用监督标签包括： + +```text +Y_seg: liver/tumor voxel labels +Y_response: TACE response class +Y_progression: binary progression label +Y_survival: PFS / OS / TTP time-to-event labels +C: structured clinical variables +M_phase: phase availability mask +M_clinical: clinical missingness mask +``` + +模型需要预测： + +```text +P_seg = liver/tumor segmentation probabilities +P_response = response class probabilities +P_progression = progression probability +H_pfs, H_os, H_ttp = discrete-time survival hazard logits +U_phase = phase utility scores +``` + +### 3.2 数据队列 + +当前项目使用三个公开数据源，每个数据源承担不同角色： + +| Dataset | Role | Used for Training | Used for Validation | Labels Used | +|---|---|---:|---:|---| +| MSD/LiTS liver tumor data | segmentation pretraining | yes | yes | liver/tumor masks | +| WAW-TACE | main model development | yes | yes | multiphase CT, masks, clinical variables, response/progression/survival | +| HCC-TACE-Seg | external validation | no | yes | CT and liver/tumor masks | + +WAW-TACE clean benchmark 共 233 例，划分为 187 例训练和 46 例内部验证。划分设置为 `seed=2026`、`val_fraction=0.2`，并按 `label_progression` 分层。HCC-TACE-Seg 外部验证集包含 105 例，只用于 segmentation validation。 + +### 3.3 数据预处理 + +所有数据被转换成 NPZ training caches。CT 强度经过肝窗相关 clipping 和归一化。训练时将 volume 和 label crop/pad 到固定 patch size： + +```text +patch_shape = [64, 96, 96] +``` + +对 WAW-TACE，四个 CT phase 被存为四个输入通道： + +```text +channel 0: native / non-contrast CT +channel 1: arterial phase +channel 2: portal venous phase +channel 3: delayed phase +``` + +缺失期相用 0 volume 填充，并额外输入 `phase_available` mask。临床变量从 clean clinical table 中读取，使用训练集统计量标准化，并与 missingness mask 拼接。 + +对 HCC-TACE-Seg 外部验证，该数据集只有单期 CT。由于 WAW 训练出的模型需要四通道输入，当前评估采用： + +```text +repeat_1ch_to_4ch +``` + +也就是把同一个 HCC CT volume 复制到四个输入通道。这可以保证 checkpoint 结构不变，但应理解为一种外部适配协议，而不是真实多期 CT 评估。 + +### 3.4 网络结构 + +当前 full model 使用 MONAI SegResNet-style 3D backbone。共享 encoder 同时服务 segmentation 和 patient-level prediction。 + +```text +Multiphase CT volumes + | + v +Shared 3D SegResNet encoder + | + +--> Segmentation decoder --> liver/tumor masks + | + +--> pooled image features + | + +--> phase statistics and phase utility + | + +--> clinical encoder with missingness mask + | + v +Fusion MLP + | + +--> response head + +--> progression head + +--> PFS hazard head + +--> OS hazard head + +--> TTP hazard head +``` + +Segmentation decoder 输出三类： + +```text +0: background +1: liver +2: tumor +``` + +Phase utility module 接收 pooled backbone features 和 phase availability mask，并输出 available CT phases 上的 soft utility distribution： + +```text +U_phase = softmax(f_phase(z, M_phase)) +``` + +Clinical encoder 的输入是： + +```text +[clinical_values, clinical_missingness_mask] +``` + +这样做可以避免模型把真实的 0 值临床变量和缺失值混淆。 + +### 3.5 损失函数 + +训练目标由 segmentation、classification、survival、utility 和 calibration losses 组成： + +```text +L = λ_seg L_seg + + λ_cls L_cls + + λ_surv L_surv + + λ_util L_util + + λ_cal L_cal +``` + +当前 clean benchmark 使用的权重为： + +```text +λ_seg = 1.0 +λ_cls = 1.0 +λ_surv = 0.5 +λ_util = 0.2 +λ_cal = 0.05 +``` + +Segmentation 使用 Dice + cross entropy，并加入 class weights。Response 使用 multiclass cross entropy。Progression 使用 binary classification loss。PFS、OS 和 TTP 被建模为 fixed time bins 上的 discrete-time survival tasks。 + +### 3.6 对比方法 + +当前 benchmark 包含四类方法： + +| Group | Methods | Purpose | +|---|---|---| +| Clinical/tabular baselines | clinical logistic, radiomics SVM, radiomics + clinical RF | 检验简单 clinical/radiomics features 是否足够 | +| CT-only outcome baselines | 3D ResNet18, 3D DenseNet121 | 检验不做 segmentation multitask 时 CT 对 outcome 的预测能力 | +| Segmentation baselines | SegResNet seg-only, nnU-Net ResEnc, TransUNet, SegMamba, Swin-UMamba, HybridMamba | 比较 liver/tumor segmentation 能力 | +| Proposed model | full multitask model | 联合分割、临床融合、response/progression/survival 预测 | + +## 4. Experiments + +### 4.1 实验设置 + +主内部 benchmark 在 WAW-TACE 上完成： + +| Split | Cases | Progression 0 | Progression 1 | +|---|---:|---:|---:| +| Train | 187 | 142 | 45 | +| Validation | 46 | 35 | 11 | + +外部 benchmark 在 HCC-TACE-Seg 上完成： + +| Dataset | Cases | Use | +|---|---:|---| +| HCC-TACE-Seg | 105 | external liver/tumor segmentation validation | + +主要评估指标包括： + +| Task | Metrics | +|---|---| +| Segmentation | Dice, IoU, ASSD, NSD | +| Response classification | accuracy, balanced accuracy, macro-F1 | +| Progression prediction | AUC, AP, Brier, ECE, balanced accuracy | +| Survival prediction | PFS/OS/TTP C-index | + +### 4.2 WAW-TACE 内部验证 + +当前 WAW-TACE clean benchmark 总结如下： + +| Method | Task Type | Liver Dice | Tumor Dice | Mean FG Dice | Response Acc | Progression AUC | PFS C-index | OS C-index | TTP C-index | +|---|---|---:|---:|---:|---:|---:|---:|---:|---:| +| Clinical-only logistic | tabular outcome | - | - | - | 0.478 | 0.582 | 0.473 | 0.710 | 0.473 | +| Radiomics SVM | radiomics outcome | - | - | - | 0.348 | 0.499 | 0.389 | 0.636 | 0.389 | +| Radiomics + clinical RF | radiomics + clinical | - | - | - | 0.609 | 0.496 | 0.284 | 0.582 | 0.284 | +| 3D ResNet18 CT-only | deep CT outcome | - | - | - | 0.587 | 0.706 | 0.821 | 0.565 | 0.816 | +| 3D DenseNet121 CT-only | deep CT outcome | - | - | - | 0.587 | 0.630 | 0.800 | 0.491 | 0.711 | +| SegResNet seg-only | segmentation | 0.910 | 0.631 | 0.771 | - | - | - | - | - | +| Full multitask, ours | multitask | 0.917 | 0.645 | 0.781 | 0.630 | 0.696 | 0.389 | 0.663 | 0.379 | + +当前 full multitask model 在主 benchmark 中取得最好的内部 segmentation performance，mean foreground Dice 为 0.781，tumor Dice 为 0.645。不过，outcome 和 survival 结果并不完全理想。尤其是 CT-only ResNet18 在 PFS 和 TTP C-index 上强于当前 full multitask model。因此，目前结果更有力地支持 segmentation 和 anatomical support-map 相关部分，而 survival modeling 部分还需要继续加强。 + +### 4.3 WAW-TACE 上的近期分割基线 + +我们还在相同 WAW-TACE internal split 上评估了近期 segmentation-oriented methods。 + +| Method | Venue/Source | Parameters | Liver Dice | Tumor Dice | Mean FG Dice | Mean FG IoU | +|---|---|---:|---:|---:|---:|---:| +| nnU-Net ResEnc 2024 | MICCAI 2024 | 17.42M | 0.913 | 0.590 | 0.751 | 0.663 | +| TransUNet 2024 | Medical Image Analysis 2024 | 6.71M | 0.904 | 0.560 | 0.732 | 0.642 | +| SegMamba 2024 | MICCAI 2024 | 8.27M | 0.918 | 0.623 | 0.771 | 0.678 | +| Swin-UMamba 2024 | MICCAI 2024 | 8.22M | 0.916 | 0.605 | 0.760 | 0.670 | +| HybridMamba 2025 | MICCAI 2025 | 8.63M | 0.919 | 0.563 | 0.741 | 0.654 | +| Full multitask, ours | this work | 18.92M | 0.917 | 0.645 | 0.781 | 0.688 | + +在近期 segmentation baselines 中，SegMamba 的 mean foreground Dice 最好，接近 SegResNet-only baseline。本文 full multitask model 在该内部比较中取得最高的 tumor Dice 和 mean foreground Dice。 + +### 4.4 HCC-TACE-Seg 外部验证 + +HCC-TACE-Seg 用作外部分割验证数据集。由于当前工程设置下它没有 WAW 风格的 outcome 和 survival 标签，所以这里只报告 segmentation metrics。 + +| Method | Liver Dice | Tumor Dice | Mean FG Dice | Mean FG IoU | Liver NSD | Tumor NSD | +|---|---:|---:|---:|---:|---:|---:| +| Full multitask, ours | 0.758 | 0.570 | 0.664 | 0.546 | 0.662 | 0.578 | +| SegResNet seg-only | 0.731 | 0.475 | 0.603 | 0.490 | 0.618 | 0.486 | +| SegMamba 2024 | 0.725 | 0.261 | 0.493 | 0.389 | 0.645 | 0.251 | +| HybridMamba 2025 | 0.720 | 0.265 | 0.492 | 0.389 | 0.619 | 0.268 | +| Swin-UMamba 2024 | 0.700 | 0.245 | 0.472 | 0.370 | 0.638 | 0.242 | +| TransUNet 2024 | 0.596 | 0.290 | 0.443 | 0.338 | 0.594 | 0.291 | +| nnU-Net ResEnc 2024 | 0.612 | 0.223 | 0.417 | 0.318 | 0.617 | 0.228 | + +本文 full multitask model 在外部验证中取得最佳 segmentation performance，tumor Dice 为 0.570，mean foreground Dice 为 0.664。这说明，即使模型只在 WAW-TACE 上训练，在 HCC-TACE-Seg 外部队列上直接推理时，多任务训练得到的 liver/tumor representation 仍具有较好的迁移能力。 + +不过，外部结果仍低于 WAW 内部验证： + +| Test Set | Liver Dice | Tumor Dice | Mean FG Dice | Interpretation | +|---|---:|---:|---:|---| +| WAW-TACE internal validation | 0.917 | 0.645 | 0.781 | same-cohort internal split | +| HCC-TACE-Seg external validation | 0.758 | 0.570 | 0.664 | cross-cohort external validation | + +这个差距说明 WAW-TACE 与 HCC-TACE-Seg 之间存在 domain shift。可能原因包括 CT acquisition protocol 不同、phase availability 不同、annotation style 不同、肿瘤大小分布不同，以及外部验证时使用了 single-phase-to-four-channel adaptation。 + +### 4.5 Phase Ablation + +项目中还包含一组 phase ablation experiment。该实验使用的是较早的 multitask 配置，因此更适合作为 phase-analysis result，而不是最终公平主表。 + +| Phase Mode | Mean FG Dice | Tumor Dice | Progression AUC | Response Acc | +|---|---:|---:|---:|---:| +| all phases | 0.772 | 0.618 | 1.000 | 0.957 | +| native only | 0.391 | 0.030 | 1.000 | 0.957 | +| arterial only | 0.595 | 0.347 | 1.000 | 0.935 | +| portal only | 0.678 | 0.460 | 1.000 | 0.957 | +| delayed only | 0.614 | 0.402 | 1.000 | 0.957 | +| utility top1 | 0.435 | 0.062 | 1.000 | 0.957 | + +主要观察是：all-phase input 对 segmentation 最好，而 portal phase 是最强单期。当前 `utility_top1` 对 segmentation 较弱，说明目前学到的 phase utility 可能更偏向 outcome signal，而不是 segmentation quality。后续版本应该将 segmentation-oriented phase utility 和 outcome-oriented phase utility 分开建模。 + +### 4.6 当前局限 + +当前工作还不是最终可投稿版本，仍有几个重要局限： + +1. **Outcome 和 survival 性能仍不够强。** 当前 full multitask model 在 PFS/TTP C-index 上没有超过 CT-only ResNet18。 +2. **完整消融尚未完成。** 目前只完成了 phase ablation。去掉 clinical variables、missingness masks、survival heads、support maps 和 pretraining 等计划消融仍需补齐。 +3. **外部验证目前只覆盖 segmentation。** HCC-TACE-Seg 当前只能支持外部 mask evaluation，不能验证 response/progression/survival。 +4. **单期外部适配并不完美。** HCC-TACE-Seg CT 被复制到四个通道，这是一种实用适配，但不等价于真实多期 CT。 +5. **近期 Mamba baselines 是工程复现/适配。** 当前环境没有使用 official `mamba_ssm` kernels 或 official pretrained weights，因此这些方法应描述为 repository-native reproductions/adapters，而不是严格官方复现。 + +## 5. Discussion + +当前证据表明，CT-first multitask model 对 HCC-TACE 分析是有潜力的，尤其是在 segmentation 和 external transfer 方面。与 segmentation-only SegResNet 相比，full multitask model 在 WAW internal validation 和 HCC external validation 上都提升了 tumor Dice。这是一个重要信号：即使 outcome heads 目前还没有完全优化，多任务监督也可能促使 backbone 学到更可迁移的 tumor representation。 + +同时，clean benchmark 也诚实地暴露了当前弱点。CT-only ResNet18 目前在 PFS 和 TTP C-index 上优于 full multitask model。这意味着最终论文不能声称当前模型已经解决了 prognosis prediction。下一步工程重点应放在 survival head design、loss balancing、risk calibration 和 multi-seed validation 上。如果 survival 仍然较弱，论文主线可以收缩到 segmentation-supported response/progression modeling，而降低 survival claim 的强度。 + +外部验证结果对论文叙事很有价值。模型只在 WAW-TACE 上训练，然后在 HCC-TACE-Seg 上不 fine-tune 直接评估，却在 external tumor segmentation 上优于所有已评估 baseline。这支持一个合理 claim：anatomical multitask representation 能提升跨队列 segmentation robustness。不过 domain gap 仍然明显，后续应该探索 domain adaptation、external fine-tuning 或更强的 CT pretraining。 + +## 6. Conclusion + +本文提出一个面向 HCC-TACE 的队列感知多期 CT-first 框架，将 segmentation、response characterization、progression prediction 和 survival estimation 统一到一个共享 3D 模型中，并结合 liver/tumor support maps、phase utility estimation 和 clinical fusion。当前实验显示，full multitask model 在 WAW-TACE 内部验证和 HCC-TACE-Seg 外部验证上均取得最好的 tumor Dice 和 mean foreground Dice。Outcome 和 survival prediction 仍处于开发阶段，需要进一步消融、校准和验证后，才能形成最终临床结论。 + +## 后续需要补充的正式参考文献 + +最终论文至少需要补充以下 BibTeX 引用： + +1. WAW-TACE dataset, Radiology: Artificial Intelligence 2024. +2. MSD Task03 Liver / LiTS liver tumor segmentation datasets. +3. SegResNet / autoencoder-regularized 3D U-Net. +4. nnU-Net, Nature Methods 2021. +5. nnU-Net Revisited, MICCAI 2024. +6. TransUNet, Medical Image Analysis 2024. +7. SegMamba, MICCAI 2024. +8. Swin-UMamba, MICCAI 2024. +9. HybridMamba, MICCAI 2025. +10. 用作 outcome baseline 的 HCC/TACE radiomics 和 clinical nomogram papers。 + +## 当前文件映射 + +本文使用的关键工程文件： + +| Artifact | Path | +|---|---| +| Main plan | `plan.md` | +| Benchmark notes | `对比方法Benchmark.md` | +| WAW benchmark summary | `experiments/benchmark/benchmark_summary.csv` | +| Recent segmentation benchmark | `experiments/recent_benchmark/benchmark_summary.csv` | +| HCC external benchmark | `experiments/hcc_external_benchmark/benchmark_summary.csv` | +| Main multitask config | `configs/benchmark/full_multitask_clean.yaml` | +| HCC external benchmark config | `configs/hcc_external_benchmark.yaml` | +| Main model code | `src/liver_tace/model.py` | +| HCC external benchmark code | `src/liver_tace/hcc_external_benchmark.py` | + diff --git a/CT/liver/scripts/build_hcc_tace_seg_cache.py b/CT/liver/scripts/build_hcc_tace_seg_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..a5bea52237cec56b88666d348e58a954959bb3ab --- /dev/null +++ b/CT/liver/scripts/build_hcc_tace_seg_cache.py @@ -0,0 +1,382 @@ +#!/usr/bin/env python3 +"""Convert HCC-TACE-Seg DICOM CT + DICOM-SEG to training-ready NPZ caches.""" + +from __future__ import annotations + +import argparse +from collections import Counter +from pathlib import Path + +import numpy as np +import pandas as pd +import pydicom +import SimpleITK as sitk + + +HU_MIN = -150.0 +HU_MAX = 400.0 + + +def rel(path: Path, root: Path) -> str: + path = path if path.is_absolute() else path.absolute() + root = root if root.is_absolute() else root.absolute() + try: + return str(path.relative_to(root)) + except ValueError: + return str(path) + + +def parse_spacing(value: str) -> tuple[float, float, float]: + parts = [float(x) for x in value.split(",")] + if len(parts) == 1: + return (parts[0], parts[0], parts[0]) + if len(parts) != 3: + raise ValueError("--spacing must be a single value or three comma-separated values") + return tuple(parts) + + +def normalize_ct(array: np.ndarray) -> np.ndarray: + array = np.clip(array.astype(np.float32), HU_MIN, HU_MAX) + return ((array - HU_MIN) / (HU_MAX - HU_MIN)).astype(np.float16) + + +def bbox_from_mask(mask: np.ndarray, margin_vox: tuple[int, int, int]) -> tuple[slice, slice, slice]: + coords = np.argwhere(mask > 0) + if coords.size == 0: + return tuple(slice(0, s) for s in mask.shape) # type: ignore[return-value] + lo = coords.min(axis=0) + hi = coords.max(axis=0) + 1 + for axis in range(3): + lo[axis] = max(0, lo[axis] - margin_vox[axis]) + hi[axis] = min(mask.shape[axis], hi[axis] + margin_vox[axis]) + return slice(lo[0], hi[0]), slice(lo[1], hi[1]), slice(lo[2], hi[2]) + + +def read_meta(path: Path) -> pydicom.Dataset: + return pydicom.dcmread(str(path), stop_before_pixels=True, force=True) + + +def get_frame_position(frame_group: pydicom.Dataset) -> tuple[float, float, float] | None: + if hasattr(frame_group, "PlanePositionSequence") and frame_group.PlanePositionSequence: + return tuple(float(x) for x in frame_group.PlanePositionSequence[0].ImagePositionPatient) + return None + + +def get_frame_source_uid(frame_group: pydicom.Dataset) -> str | None: + for deriv in getattr(frame_group, "DerivationImageSequence", []): + for source in getattr(deriv, "SourceImageSequence", []): + uid = getattr(source, "ReferencedSOPInstanceUID", None) + if uid: + return str(uid) + return None + + +def collect_ct_sibling_files(seg_dir: Path) -> dict[str, list[Path]]: + study_dir = seg_dir.parent + series_files: dict[str, list[Path]] = {} + for series_dir in sorted(p for p in study_dir.iterdir() if p.is_dir() and p != seg_dir): + files = sorted(series_dir.glob("*.dcm")) + if not files: + continue + try: + meta = read_meta(files[0]) + except Exception: + continue + if getattr(meta, "Modality", "") != "CT": + continue + series_files[str(series_dir)] = files + return series_files + + +def choose_ct_series(seg_ds: pydicom.Dataset, seg_dir: Path) -> tuple[str, list[Path], dict[str, Path]]: + source_uids = [] + frame_z = [] + for frame_group in getattr(seg_ds, "PerFrameFunctionalGroupsSequence", []): + uid = get_frame_source_uid(frame_group) + if uid: + source_uids.append(uid) + position = get_frame_position(frame_group) + if position is not None: + frame_z.append(round(float(position[2]), 2)) + + best_series = "" + best_files: list[Path] = [] + best_uid_to_file: dict[str, Path] = {} + best_score = (-1, -1, -1) + for series_key, files in collect_ct_sibling_files(seg_dir).items(): + uid_to_file = {} + ct_z = [] + for path in files: + try: + ds = read_meta(path) + except Exception: + continue + uid_to_file[str(ds.SOPInstanceUID)] = path + if hasattr(ds, "ImagePositionPatient"): + ct_z.append(round(float(ds.ImagePositionPatient[2]), 2)) + source_score = sum(uid in uid_to_file for uid in set(source_uids)) + z_score = len(set(frame_z) & set(ct_z)) + # Some TCIA SEG objects have absent or sparse SourceImageSequence. In + # that case, z-position overlap is more reliable than a single UID hit. + source_rank = source_score if source_score > 1 else 0 + score = (source_rank, z_score, len(files)) + if score > best_score: + best_score = score + best_series = series_key + best_files = files + best_uid_to_file = uid_to_file + + if not best_files: + raise RuntimeError(f"No sibling CT series found for {seg_dir}") + return best_series, best_files, best_uid_to_file + + +def load_ct_volume(files: list[Path]) -> tuple[np.ndarray, list[pydicom.Dataset], np.ndarray, tuple[float, float, float], tuple[float, ...]]: + records = [] + for path in files: + ds = pydicom.dcmread(str(path), force=True) + position = np.asarray([float(x) for x in ds.ImagePositionPatient], dtype=np.float64) + orientation = np.asarray([float(x) for x in ds.ImageOrientationPatient], dtype=np.float64) + row_cos = orientation[:3] + col_cos = orientation[3:] + normal = np.cross(row_cos, col_cos) + projection = float(np.dot(position, normal)) + records.append((projection, path, ds, position, orientation, normal)) + records.sort(key=lambda x: x[0]) + + arrays = [] + metas = [] + positions = [] + for _, _, ds, position, _, _ in records: + arr = ds.pixel_array.astype(np.float32) + slope = float(getattr(ds, "RescaleSlope", 1.0)) + intercept = float(getattr(ds, "RescaleIntercept", 0.0)) + arrays.append(arr * slope + intercept) + metas.append(ds) + positions.append(position) + + first = records[0] + spacing_y, spacing_x = [float(x) for x in metas[0].PixelSpacing] + if len(records) > 1: + spacing_z = float(np.median(np.diff([r[0] for r in records]))) + spacing_z = abs(spacing_z) if spacing_z else float(getattr(metas[0], "SliceThickness", 1.0)) + else: + spacing_z = float(getattr(metas[0], "SliceThickness", 1.0)) + spacing_xyz = (spacing_x, spacing_y, spacing_z) + row_cos = first[4][:3] + col_cos = first[4][3:] + normal = first[5] + direction = ( + float(row_cos[0]), float(col_cos[0]), float(normal[0]), + float(row_cos[1]), float(col_cos[1]), float(normal[1]), + float(row_cos[2]), float(col_cos[2]), float(normal[2]), + ) + return np.stack(arrays, axis=0), metas, np.stack(positions, axis=0), spacing_xyz, direction + + +def make_sitk_image(array_zyx: np.ndarray, metas: list[pydicom.Dataset], spacing_xyz: tuple[float, float, float], direction: tuple[float, ...], pixel_type: int) -> sitk.Image: + image = sitk.GetImageFromArray(array_zyx.astype(np.float32 if pixel_type == sitk.sitkFloat32 else np.uint8)) + image.SetSpacing(spacing_xyz) + image.SetOrigin(tuple(float(x) for x in metas[0].ImagePositionPatient)) + image.SetDirection(direction) + return image + + +def make_reference_grid(image: sitk.Image, spacing_xyz: tuple[float, float, float]) -> sitk.Image: + original_spacing = image.GetSpacing() + original_size = image.GetSize() + size = [max(1, int(round(original_size[i] * original_spacing[i] / spacing_xyz[i]))) for i in range(3)] + ref = sitk.Image(size, image.GetPixelID()) + ref.SetOrigin(image.GetOrigin()) + ref.SetSpacing(spacing_xyz) + ref.SetDirection(image.GetDirection()) + return ref + + +def resample(image: sitk.Image, ref: sitk.Image, interpolator: int, default: float, pixel_type: int) -> sitk.Image: + return sitk.Resample(image, ref, sitk.Transform(), interpolator, default, pixel_type) + + +def segment_label_map(seg_ds: pydicom.Dataset) -> dict[int, int]: + mapping = {} + for seg in getattr(seg_ds, "SegmentSequence", []): + number = int(seg.SegmentNumber) + label = str(getattr(seg, "SegmentLabel", "")).lower() + if "liver" in label: + mapping[number] = 1 + elif any(token in label for token in ["mass", "tumor", "tumour", "lesion"]): + mapping[number] = 2 + return mapping + + +def build_label_volume(seg_ds: pydicom.Dataset, metas: list[pydicom.Dataset], uid_to_index: dict[str, int]) -> np.ndarray: + label = np.zeros((len(metas), int(seg_ds.Rows), int(seg_ds.Columns)), dtype=np.uint8) + seg_map = segment_label_map(seg_ds) + pixel = seg_ds.pixel_array + if pixel.ndim == 2: + pixel = pixel[None, ...] + + position_to_index = {} + z_to_index = {} + for idx, meta in enumerate(metas): + key = tuple(round(float(x), 3) for x in meta.ImagePositionPatient) + position_to_index[key] = idx + z_to_index[round(float(meta.ImagePositionPatient[2]), 2)] = idx + + for frame_idx, frame_group in enumerate(seg_ds.PerFrameFunctionalGroupsSequence): + seg_num = int(frame_group.SegmentIdentificationSequence[0].ReferencedSegmentNumber) + target_label = seg_map.get(seg_num) + if target_label is None: + continue + + slice_index = None + source_uid = get_frame_source_uid(frame_group) + if source_uid and source_uid in uid_to_index: + slice_index = uid_to_index[source_uid] + if slice_index is None: + position = get_frame_position(frame_group) + if position is not None: + key = tuple(round(float(x), 3) for x in position) + slice_index = position_to_index.get(key) + if slice_index is None: + slice_index = z_to_index.get(round(float(position[2]), 2)) + if slice_index is None: + continue + + mask = pixel[frame_idx] > 0 + if target_label == 1: + label[slice_index][mask] = np.maximum(label[slice_index][mask], 1) + elif target_label == 2: + label[slice_index][mask] = 2 + return label + + +def process_seg_case( + row: pd.Series, + root: Path, + out_dir: Path, + spacing_xyz: tuple[float, float, float], + margin_mm: float, + overwrite: bool, + compressed: bool, +) -> dict[str, object]: + seg_dir = root / str(row["dicom_series_dir"]) + seg_file = next(seg_dir.glob("*.dcm")) + out_path = out_dir / f"{row['patient_id']}_{row.name:03d}.npz" + if out_path.exists() and not overwrite: + with np.load(out_path) as data: + image_shape = data["image"].shape + has_liver = bool((data["label"] == 1).any()) + has_tumor = bool((data["label"] == 2).any()) + return { + "dataset": "hcc_tace_seg", + "patient_id": row["patient_id"], + "case_id": row["case_id"], + "npz_path": rel(out_path, root), + "shape_c": image_shape[0], + "shape_z": image_shape[1], + "shape_y": image_shape[2], + "shape_x": image_shape[3], + "has_liver_mask": int(has_liver), + "has_tumor_mask": int(has_tumor), + "skipped_existing": 1, + } + + seg_ds = pydicom.dcmread(str(seg_file), force=True) + ct_series_key, ct_files, uid_to_file = choose_ct_series(seg_ds, seg_dir) + ct_array, metas, _, ct_spacing, direction = load_ct_volume(ct_files) + uid_to_index = {str(ds.SOPInstanceUID): i for i, ds in enumerate(metas)} + label = build_label_volume(seg_ds, metas, uid_to_index) + + image_sitk = make_sitk_image(ct_array, metas, ct_spacing, direction, sitk.sitkFloat32) + label_sitk = make_sitk_image(label, metas, ct_spacing, direction, sitk.sitkUInt8) + ref = make_reference_grid(image_sitk, spacing_xyz) + image_resampled = resample(image_sitk, ref, sitk.sitkLinear, HU_MIN, sitk.sitkFloat32) + label_resampled = resample(label_sitk, ref, sitk.sitkNearestNeighbor, 0, sitk.sitkUInt8) + image_arr = normalize_ct(sitk.GetArrayFromImage(image_resampled)) + label_arr = sitk.GetArrayFromImage(label_resampled).astype(np.uint8) + + margin_vox = tuple(max(1, int(round(margin_mm / s))) for s in spacing_xyz[::-1]) + crop = bbox_from_mask(label_arr > 0, margin_vox) + image_arr = image_arr[crop][None, ...].astype(np.float16) + label_arr = label_arr[crop].astype(np.uint8) + + out_path.parent.mkdir(parents=True, exist_ok=True) + saver = np.savez_compressed if compressed else np.savez + saver( + out_path, + image=image_arr, + label=label_arr, + liver_mask=(label_arr == 1).astype(np.uint8), + tumor_mask=(label_arr == 2).astype(np.uint8), + spacing=np.asarray(spacing_xyz, dtype=np.float32), + crop_start=np.asarray([crop[0].start, crop[1].start, crop[2].start], dtype=np.int32), + crop_stop=np.asarray([crop[0].stop, crop[1].stop, crop[2].stop], dtype=np.int32), + ) + + seg_counts = Counter(int(x.ReferencedSegmentNumber) for x in [ + fg.SegmentIdentificationSequence[0] for fg in seg_ds.PerFrameFunctionalGroupsSequence + ]) + return { + "dataset": "hcc_tace_seg", + "patient_id": row["patient_id"], + "case_id": row["case_id"], + "series_uid": row["series_uid"], + "npz_path": rel(out_path, root), + "ct_series_dir": rel(Path(ct_series_key), root), + "seg_series_dir": row["dicom_series_dir"], + "spacing_x": spacing_xyz[0], + "spacing_y": spacing_xyz[1], + "spacing_z": spacing_xyz[2], + "shape_c": image_arr.shape[0], + "shape_z": image_arr.shape[1], + "shape_y": image_arr.shape[2], + "shape_x": image_arr.shape[3], + "has_liver_mask": int((label_arr == 1).any()), + "has_tumor_mask": int((label_arr == 2).any()), + "segment_frame_counts": dict(seg_counts), + "skipped_existing": 0, + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--project-root", type=Path, default=Path(__file__).resolve().parents[1]) + parser.add_argument("--spacing", default="2.0") + parser.add_argument("--margin-mm", type=float, default=20.0) + parser.add_argument("--limit", type=int, default=0) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--compressed", action="store_true") + parser.add_argument("--progress-every", type=int, default=10) + args = parser.parse_args() + + root = args.project_root.resolve() + spacing_xyz = parse_spacing(args.spacing) + manifest = pd.read_csv(root / "manifests" / "hcc_tace_seg_series_manifest.csv") + seg_rows = manifest[manifest["modality"] == "SEG"].reset_index(drop=True) + out_dir = root / "data" / "processed_training" / "hcc_tace_seg_npz" + rows = [] + errors = [] + for idx, row in seg_rows.iterrows(): + if args.limit and len(rows) >= args.limit: + break + try: + result = process_seg_case(row, root, out_dir, spacing_xyz, args.margin_mm, args.overwrite, args.compressed) + rows.append(result) + except Exception as exc: + errors.append({"patient_id": row.get("patient_id", ""), "seg_series_dir": row.get("dicom_series_dir", ""), "error": repr(exc)}) + if (idx + 1) % args.progress_every == 0: + print(f"HCC cached {len(rows)}/{len(seg_rows)} errors={len(errors)}") + + out_manifest = pd.DataFrame(rows) + out_path = root / "manifests" / "hcc_tace_seg_training_manifest.csv" + out_manifest.to_csv(out_path, index=False) + print(f"Wrote {out_path} ({len(out_manifest)} rows)") + if errors: + err_path = root / "logs" / "hcc_tace_seg_cache_errors.csv" + pd.DataFrame(errors).to_csv(err_path, index=False) + print(f"Wrote {err_path} ({len(errors)} errors)") + + +if __name__ == "__main__": + main() diff --git a/CT/liver/scripts/build_training_cache.py b/CT/liver/scripts/build_training_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..c49b1c33c74a58e0ed16342948c05805022af1b2 --- /dev/null +++ b/CT/liver/scripts/build_training_cache.py @@ -0,0 +1,441 @@ +#!/usr/bin/env python3 +"""Create training-ready NPZ caches from preprocessed liver manifests. + +Outputs: + data/processed_training/waw_tace_npz/*.npz + data/processed_training/msd_liver_npz/*.npz + manifests/waw_tace_training_manifest.csv + manifests/msd_liver_training_manifest.csv +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Iterable + +import numpy as np +import pandas as pd +import SimpleITK as sitk + + +PHASES = ["native", "arterial", "portal", "delayed"] +PHASE_PRIORITY = ["arterial", "portal", "native", "delayed"] +HU_MIN = -150.0 +HU_MAX = 400.0 + + +def rel(path: Path, root: Path) -> str: + path = path if path.is_absolute() else path.absolute() + root = root if root.is_absolute() else root.absolute() + try: + return str(path.relative_to(root)) + except ValueError: + return str(path) + + +def parse_spacing(value: str) -> tuple[float, float, float]: + parts = [float(x) for x in value.split(",")] + if len(parts) == 1: + return (parts[0], parts[0], parts[0]) + if len(parts) != 3: + raise ValueError("--spacing must be a single value or three comma-separated values") + return tuple(parts) + + +def nonempty(value: object) -> bool: + if value is None or pd.isna(value): + return False + return str(value).strip() != "" + + +def read_image(path: Path) -> sitk.Image: + return sitk.ReadImage(str(path)) + + +def make_reference_grid(image: sitk.Image, spacing_xyz: tuple[float, float, float]) -> sitk.Image: + original_spacing = image.GetSpacing() + original_size = image.GetSize() + size = [ + max(1, int(round(original_size[i] * original_spacing[i] / spacing_xyz[i]))) + for i in range(3) + ] + ref = sitk.Image(size, sitk.sitkFloat32) + ref.SetOrigin(image.GetOrigin()) + ref.SetSpacing(spacing_xyz) + ref.SetDirection(image.GetDirection()) + return ref + + +def resample_to_ref( + image: sitk.Image, + ref: sitk.Image, + *, + interpolator: int, + default_value: float = 0.0, + pixel_type: int | None = None, +) -> sitk.Image: + if pixel_type is None: + pixel_type = image.GetPixelID() + return sitk.Resample( + image, + ref, + sitk.Transform(), + interpolator, + default_value, + pixel_type, + ) + + +def sitk_to_array(image: sitk.Image) -> np.ndarray: + return sitk.GetArrayFromImage(image) + + +def normalize_ct(array: np.ndarray) -> np.ndarray: + array = np.clip(array.astype(np.float32), HU_MIN, HU_MAX) + array = (array - HU_MIN) / (HU_MAX - HU_MIN) + return array.astype(np.float16) + + +def bbox_from_mask(mask: np.ndarray, margin_vox: tuple[int, int, int]) -> tuple[slice, slice, slice]: + coords = np.argwhere(mask > 0) + if coords.size == 0: + return tuple(slice(0, s) for s in mask.shape) # type: ignore[return-value] + lo = coords.min(axis=0) + hi = coords.max(axis=0) + 1 + for axis in range(3): + lo[axis] = max(0, lo[axis] - margin_vox[axis]) + hi[axis] = min(mask.shape[axis], hi[axis] + margin_vox[axis]) + return slice(lo[0], hi[0]), slice(lo[1], hi[1]), slice(lo[2], hi[2]) + + +def choose_reference_phase(row: pd.Series) -> str | None: + # Prefer a phase with manual tumor supervision so the support map keeps its + # native grid before other phases are resampled onto it. + for phase in PHASES: + if int(row.get(f"phase_available_{phase}", 0)) == 1 and nonempty(row.get(f"tumor_mask_{phase}_path")): + return phase + for phase in PHASE_PRIORITY: + if int(row.get(f"phase_available_{phase}", 0)) == 1 and nonempty(row.get(f"ct_{phase}_path")): + return phase + return None + + +def save_npz(path: Path, compressed: bool, **arrays: np.ndarray) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if compressed: + np.savez_compressed(path, **arrays) + else: + np.savez(path, **arrays) + + +def process_waw_case( + row: pd.Series, + root: Path, + out_dir: Path, + spacing_xyz: tuple[float, float, float], + margin_mm: float, + overwrite: bool, + compressed: bool, +) -> dict[str, object] | None: + patient_id = str(row["patient_id"]) + out_path = out_dir / f"{patient_id}.npz" + if out_path.exists() and not overwrite: + return { + "dataset": "waw_tace", + "patient_id": patient_id, + "case_id": patient_id, + "npz_path": rel(out_path, root), + "skipped_existing": 1, + } + + ref_phase = choose_reference_phase(row) + if ref_phase is None: + return None + ref_ct_path = root / str(row[f"ct_{ref_phase}_path"]) + ref_grid = make_reference_grid(read_image(ref_ct_path), spacing_xyz) + margin_vox = tuple(max(1, int(round(margin_mm / s))) for s in spacing_xyz[::-1]) + + image_channels = [] + phase_available = [] + liver_union = np.zeros(ref_grid.GetSize()[::-1], dtype=bool) + tumor_union = np.zeros(ref_grid.GetSize()[::-1], dtype=bool) + + for phase in PHASES: + ct_path_value = row.get(f"ct_{phase}_path") + if nonempty(ct_path_value): + ct = read_image(root / str(ct_path_value)) + ct_resampled = resample_to_ref( + ct, + ref_grid, + interpolator=sitk.sitkLinear, + default_value=HU_MIN, + pixel_type=sitk.sitkFloat32, + ) + image_channels.append(normalize_ct(sitk_to_array(ct_resampled))) + phase_available.append(1) + else: + image_channels.append(np.zeros(ref_grid.GetSize()[::-1], dtype=np.float16)) + phase_available.append(0) + + liver_path_value = row.get(f"liver_mask_{phase}_path") + if nonempty(liver_path_value): + liver = read_image(root / str(liver_path_value)) + liver_resampled = resample_to_ref( + liver, + ref_grid, + interpolator=sitk.sitkNearestNeighbor, + default_value=0, + pixel_type=sitk.sitkUInt8, + ) + liver_union |= sitk_to_array(liver_resampled) > 0 + + tumor_path_value = row.get(f"tumor_mask_{phase}_path") + if nonempty(tumor_path_value): + tumor = read_image(root / str(tumor_path_value)) + tumor_resampled = resample_to_ref( + tumor, + ref_grid, + interpolator=sitk.sitkNearestNeighbor, + default_value=0, + pixel_type=sitk.sitkUInt8, + ) + tumor_union |= sitk_to_array(tumor_resampled) > 0 + + crop_source = liver_union | tumor_union + crop = bbox_from_mask(crop_source, margin_vox) + image = np.stack([channel[crop] for channel in image_channels], axis=0).astype(np.float16) + liver_mask = liver_union[crop].astype(np.uint8) + tumor_mask = tumor_union[crop].astype(np.uint8) + + clinical_cols = [ + c for c in row.index + if not c.endswith("_missing") + and c not in set(["dataset", "patient_id", "case_id"]) + and not c.endswith("_path") + and not c.startswith("ct_") + and not c.startswith("organ_mask_") + and not c.startswith("liver_mask_") + and not c.startswith("tumor_mask_") + ] + numeric = pd.to_numeric(row[clinical_cols], errors="coerce") + clinical_values = numeric.to_numpy(dtype=np.float32) + clinical_missing = np.isnan(clinical_values).astype(np.uint8) + clinical_values = np.nan_to_num(clinical_values, nan=0.0) + + save_npz( + out_path, + compressed, + image=image, + liver_mask=liver_mask, + tumor_mask=tumor_mask, + phase_available=np.asarray(phase_available, dtype=np.uint8), + clinical_values=clinical_values, + clinical_missing=clinical_missing, + spacing=np.asarray(spacing_xyz, dtype=np.float32), + crop_start=np.asarray([crop[0].start, crop[1].start, crop[2].start], dtype=np.int32), + crop_stop=np.asarray([crop[0].stop, crop[1].stop, crop[2].stop], dtype=np.int32), + label_response=np.asarray([row.get("label_response", np.nan)], dtype=np.float32), + label_progression=np.asarray([row.get("label_progression", np.nan)], dtype=np.float32), + time_pfs=np.asarray([row.get("time_pfs", np.nan)], dtype=np.float32), + event_pfs=np.asarray([row.get("event_pfs", np.nan)], dtype=np.float32), + time_os=np.asarray([row.get("time_os", np.nan)], dtype=np.float32), + event_os=np.asarray([row.get("event_os", np.nan)], dtype=np.float32), + time_ttp=np.asarray([row.get("time_ttp", np.nan)], dtype=np.float32), + event_ttp=np.asarray([row.get("event_ttp", np.nan)], dtype=np.float32), + ) + + return { + "dataset": "waw_tace", + "patient_id": patient_id, + "case_id": patient_id, + "npz_path": rel(out_path, root), + "reference_phase": ref_phase, + "spacing_x": spacing_xyz[0], + "spacing_y": spacing_xyz[1], + "spacing_z": spacing_xyz[2], + "shape_c": image.shape[0], + "shape_z": image.shape[1], + "shape_y": image.shape[2], + "shape_x": image.shape[3], + "phase_available_native": phase_available[0], + "phase_available_arterial": phase_available[1], + "phase_available_portal": phase_available[2], + "phase_available_delayed": phase_available[3], + "has_liver_mask": int(liver_mask.any()), + "has_tumor_mask": int(tumor_mask.any()), + "label_response": row.get("label_response", np.nan), + "label_progression": row.get("label_progression", np.nan), + "time_pfs": row.get("time_pfs", np.nan), + "event_pfs": row.get("event_pfs", np.nan), + "time_os": row.get("time_os", np.nan), + "event_os": row.get("event_os", np.nan), + "time_ttp": row.get("time_ttp", np.nan), + "event_ttp": row.get("event_ttp", np.nan), + "skipped_existing": 0, + } + + +def build_waw_cache(args: argparse.Namespace, root: Path) -> pd.DataFrame: + manifest = pd.read_csv(root / "manifests" / "waw_tace_manifest.csv") + out_dir = root / "data" / "processed_training" / "waw_tace_npz" + rows = [] + spacing_xyz = parse_spacing(args.spacing) + iterable: Iterable[tuple[int, pd.Series]] = manifest.iterrows() + for i, row in iterable: + if args.limit and len(rows) >= args.limit: + break + result = process_waw_case( + row, + root, + out_dir, + spacing_xyz, + args.margin_mm, + args.overwrite, + args.compressed, + ) + if result is not None: + rows.append(result) + if len(rows) % args.progress_every == 0: + print(f"WAW cached {len(rows)}/{len(manifest)}") + df = pd.DataFrame(rows) + path = root / "manifests" / "waw_tace_training_manifest.csv" + path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(path, index=False) + print(f"Wrote {path} ({len(df)} rows)") + return df + + +def process_msd_case( + row: pd.Series, + root: Path, + out_dir: Path, + spacing_xyz: tuple[float, float, float], + margin_mm: float, + overwrite: bool, + compressed: bool, +) -> dict[str, object]: + case_id = str(row["case_id"]) + out_path = out_dir / f"{case_id}.npz" + if out_path.exists() and not overwrite: + with np.load(out_path) as data: + shape = data["image"].shape + return { + "dataset": "msd_liver", + "patient_id": case_id, + "case_id": case_id, + "npz_path": rel(out_path, root), + "shape_c": shape[0], + "shape_z": shape[1], + "shape_y": shape[2], + "shape_x": shape[3], + "skipped_existing": 1, + } + + image_src = root / str(row["ct_path"]) + label_src = root / str(row["label_path"]) + image = read_image(image_src) + label = read_image(label_src) + ref_grid = make_reference_grid(image, spacing_xyz) + image_resampled = resample_to_ref( + image, + ref_grid, + interpolator=sitk.sitkLinear, + default_value=HU_MIN, + pixel_type=sitk.sitkFloat32, + ) + label_resampled = resample_to_ref( + label, + ref_grid, + interpolator=sitk.sitkNearestNeighbor, + default_value=0, + pixel_type=sitk.sitkUInt8, + ) + image_arr = normalize_ct(sitk_to_array(image_resampled)) + label_arr = sitk_to_array(label_resampled).astype(np.uint8) + margin_vox = tuple(max(1, int(round(margin_mm / s))) for s in spacing_xyz[::-1]) + crop = bbox_from_mask(label_arr > 0, margin_vox) + image_arr = image_arr[crop][None, ...].astype(np.float16) + label_arr = label_arr[crop].astype(np.uint8) + save_npz( + out_path, + compressed, + image=image_arr, + label=label_arr, + spacing=np.asarray(spacing_xyz, dtype=np.float32), + crop_start=np.asarray([crop[0].start, crop[1].start, crop[2].start], dtype=np.int32), + crop_stop=np.asarray([crop[0].stop, crop[1].stop, crop[2].stop], dtype=np.int32), + ) + return { + "dataset": "msd_liver", + "patient_id": case_id, + "case_id": case_id, + "npz_path": rel(out_path, root), + "spacing_x": spacing_xyz[0], + "spacing_y": spacing_xyz[1], + "spacing_z": spacing_xyz[2], + "shape_c": image_arr.shape[0], + "shape_z": image_arr.shape[1], + "shape_y": image_arr.shape[2], + "shape_x": image_arr.shape[3], + "has_liver_mask": int((label_arr == 1).any()), + "has_tumor_mask": int((label_arr == 2).any()), + "skipped_existing": 0, + } + + +def build_msd_cache(args: argparse.Namespace, root: Path) -> pd.DataFrame: + manifest = pd.read_csv(root / "manifests" / "msd_liver_manifest.csv") + out_dir = root / "data" / "processed_training" / "msd_liver_npz" + rows = [] + spacing_xyz = parse_spacing(args.spacing) + for _, row in manifest.iterrows(): + if args.limit and len(rows) >= args.limit: + break + rows.append( + process_msd_case( + row, + root, + out_dir, + spacing_xyz, + args.margin_mm, + args.overwrite, + args.compressed, + ) + ) + if len(rows) % args.progress_every == 0: + print(f"MSD cached {len(rows)}/{len(manifest)}") + df = pd.DataFrame(rows) + path = root / "manifests" / "msd_liver_training_manifest.csv" + path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(path, index=False) + print(f"Wrote {path} ({len(df)} rows)") + return df + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--project-root", type=Path, default=Path(__file__).resolve().parents[1]) + parser.add_argument("--dataset", choices=["waw", "msd", "all"], default="all") + parser.add_argument("--spacing", default="2.0", help="Target spacing in xyz order, e.g. 2.0 or 2.0,2.0,2.0.") + parser.add_argument("--margin-mm", type=float, default=20.0) + parser.add_argument("--limit", type=int, default=0) + parser.add_argument("--progress-every", type=int, default=10) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--compressed", action="store_true", help="Use compressed NPZ. Slower, smaller.") + args = parser.parse_args() + + root = args.project_root.resolve() + config_path = root / "data" / "processed_training" / "cache_config.json" + config_path.parent.mkdir(parents=True, exist_ok=True) + config_path.write_text(json.dumps(vars(args) | {"project_root": str(root)}, indent=2)) + + if args.dataset in {"waw", "all"}: + build_waw_cache(args, root) + if args.dataset in {"msd", "all"}: + build_msd_cache(args, root) + + +if __name__ == "__main__": + main() diff --git a/CT/liver/scripts/download_data.sh b/CT/liver/scripts/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..3d9c573773853200d3225d45572aadb5de499d2b --- /dev/null +++ b/CT/liver/scripts/download_data.sh @@ -0,0 +1,255 @@ +#!/usr/bin/env bash +set -euo pipefail + +PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +DATA_ROOT="${DATA_ROOT:-${PROJECT_ROOT}/data}" +RAW_ROOT="${RAW_ROOT:-${DATA_ROOT}/raw}" +EXTRACTED_ROOT="${EXTRACTED_ROOT:-${DATA_ROOT}/extracted}" +MANIFEST_ROOT="${MANIFEST_ROOT:-${PROJECT_ROOT}/manifests}" +LOG_ROOT="${LOG_ROOT:-${PROJECT_ROOT}/logs}" + +WAW_RAW="${RAW_ROOT}/waw_tace" +WAW_EXTRACTED="${EXTRACTED_ROOT}/waw_tace" +MSD_RAW="${RAW_ROOT}/msd_liver" +MSD_EXTRACTED="${EXTRACTED_ROOT}/msd_liver" +HCC_RAW="${RAW_ROOT}/hcc_tace_seg" +HCC_EXTRACTED="${EXTRACTED_ROOT}/hcc_tace_seg" + +HCC_TCIA_MANIFEST="${HCC_TCIA_MANIFEST:-}" +NBIA_RETRIEVER="${NBIA_RETRIEVER:-nbia-data-retriever}" + +usage() { + cat <<'EOF' +Usage: + scripts/download_data.sh [command] + +Commands: + all Download WAW-TACE and MSD Task03 Liver. HCC-TACE-Seg runs only if HCC_TCIA_MANIFEST is set. + waw-tace Download WAW-TACE from Zenodo. + extract-waw-tace + Extract WAW-TACE zip files into data/extracted/waw_tace. + msd-liver Download MSD Task03 Liver from the MONAI mirror. + hcc-tace-seg Download HCC-TACE-Seg from TCIA using NBIA Data Retriever and a .tcia manifest. + layout Create and print the planned project data layout. + +Environment variables: + DATA_ROOT Override data root. Default: /data + HCC_TCIA_MANIFEST Path to the HCC-TACE-Seg .tcia manifest downloaded from TCIA. + NBIA_RETRIEVER NBIA Data Retriever CLI path. Default: nbia-data-retriever + +Examples: + scripts/download_data.sh layout + scripts/download_data.sh waw-tace + scripts/download_data.sh extract-waw-tace + scripts/download_data.sh msd-liver + HCC_TCIA_MANIFEST=/path/to/HCC-TACE-Seg.tcia scripts/download_data.sh hcc-tace-seg + scripts/download_data.sh all +EOF +} + +require_cmd() { + local cmd="$1" + if ! command -v "${cmd}" >/dev/null 2>&1; then + echo "Missing required command: ${cmd}" >&2 + exit 1 + fi +} + +mkdir_layout() { + mkdir -p \ + "${WAW_RAW}" "${WAW_EXTRACTED}" \ + "${MSD_RAW}" "${MSD_EXTRACTED}" \ + "${HCC_RAW}" "${HCC_EXTRACTED}" \ + "${DATA_ROOT}/processed/waw_tace" \ + "${DATA_ROOT}/processed/msd_liver" \ + "${DATA_ROOT}/processed/hcc_tace_seg" \ + "${MANIFEST_ROOT}" \ + "${LOG_ROOT}" +} + +print_layout() { + mkdir_layout + cat <&1 | tee -a "${log_file}" +} + +download_waw_tace() { + require_cmd wget + require_cmd tee + mkdir_layout + + local log_file="${LOG_ROOT}/download_waw_tace.log" + echo "Writing WAW-TACE downloads to ${WAW_RAW}" + + download_file "https://zenodo.org/records/12741586/files/clinical_data_wawtace_v2_15_07_2024.xlsx?download=1" "${WAW_RAW}/clinical_data_wawtace_v2_15_07_2024.xlsx" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/ct_hcc_metadata_v2.csv?download=1" "${WAW_RAW}/ct_hcc_metadata_v2.csv" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/ct_scans_1_4_wawtace_09_05_24.zip?download=1" "${WAW_RAW}/ct_scans_1_4_wawtace_09_05_24.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/ct_scans_2_4_wawtace_09_05_24.zip?download=1" "${WAW_RAW}/ct_scans_2_4_wawtace_09_05_24.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/ct_scans_3_4_wawtace_09_05_24.zip?download=1" "${WAW_RAW}/ct_scans_3_4_wawtace_09_05_24.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/ct_scans_4_4_wawtace_09_05_24.zip?download=1" "${WAW_RAW}/ct_scans_4_4_wawtace_09_05_24.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/organ_masks_wawtace_09_05_2024.zip?download=1" "${WAW_RAW}/organ_masks_wawtace_09_05_2024.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/tumor_masks_wawtace_v1_08_05_2024.zip?download=1" "${WAW_RAW}/tumor_masks_wawtace_v1_08_05_2024.zip" "${log_file}" + download_file "https://zenodo.org/records/12741586/files/radiomics_data_wawtace_09_05_2024.xlsx?download=1" "${WAW_RAW}/radiomics_data_wawtace_09_05_2024.xlsx" "${log_file}" + + echo "WAW-TACE raw download finished." + echo "To extract: scripts/download_data.sh extract-waw-tace" +} + +extract_waw_tace() { + require_cmd unzip + mkdir_layout + + local found_zip=0 + local zip_file + + for zip_file in "${WAW_RAW}"/*.zip; do + if [[ ! -f "${zip_file}" ]]; then + continue + fi + found_zip=1 + echo "Extracting ${zip_file} to ${WAW_EXTRACTED}" + unzip -n "${zip_file}" -d "${WAW_EXTRACTED}" + done + + if [[ "${found_zip}" -eq 0 ]]; then + echo "No WAW-TACE zip files found in ${WAW_RAW}" >&2 + exit 1 + fi + + echo "WAW-TACE extraction finished." +} + +download_msd_liver() { + require_cmd wget + require_cmd tar + require_cmd tee + mkdir_layout + + local log_file="${LOG_ROOT}/download_msd_liver.log" + local archive="${MSD_RAW}/Task03_Liver.tar" + + download_file "https://msd-for-monai.s3-us-west-2.amazonaws.com/Task03_Liver.tar" "${archive}" "${log_file}" + + if [[ -d "${MSD_EXTRACTED}/Task03_Liver" ]]; then + echo "Skip existing extracted MSD data: ${MSD_EXTRACTED}/Task03_Liver" + return + fi + + echo "Extracting MSD Task03 Liver to ${MSD_EXTRACTED}" + tar -xf "${archive}" -C "${MSD_EXTRACTED}" + echo "MSD Task03 Liver finished." +} + +download_hcc_tace_seg() { + require_cmd tee + mkdir_layout + + if [[ -z "${HCC_TCIA_MANIFEST}" ]]; then + cat >&2 <&2 + exit 1 + fi + + if ! command -v "${NBIA_RETRIEVER}" >/dev/null 2>&1; then + cat >&2 <&1 | tee -a "${log_file}" + echo "HCC-TACE-Seg raw download finished." +} + +download_all() { + download_waw_tace + download_msd_liver + + if [[ -n "${HCC_TCIA_MANIFEST}" ]]; then + download_hcc_tace_seg + else + echo "Skip HCC-TACE-Seg: set HCC_TCIA_MANIFEST to enable TCIA download." + fi +} + +main() { + local command="${1:-all}" + + case "${command}" in + all) + download_all + ;; + waw-tace) + download_waw_tace + ;; + extract-waw-tace) + extract_waw_tace + ;; + msd-liver) + download_msd_liver + ;; + hcc-tace-seg) + download_hcc_tace_seg + ;; + layout) + print_layout + ;; + -h|--help|help) + usage + ;; + *) + echo "Unknown command: ${command}" >&2 + usage >&2 + exit 1 + ;; + esac +} + +main "$@" diff --git a/CT/liver/scripts/preprocess_data.py b/CT/liver/scripts/preprocess_data.py new file mode 100644 index 0000000000000000000000000000000000000000..53f34e93a32a04f99e77968cb4a86debbd1b60d6 --- /dev/null +++ b/CT/liver/scripts/preprocess_data.py @@ -0,0 +1,376 @@ +#!/usr/bin/env python3 +"""Build model-facing manifests and lightweight processed assets for liver data.""" + +from __future__ import annotations + +import argparse +import json +import os +import re +import shutil +from pathlib import Path + +import numpy as np +import pandas as pd + + +PHASES = { + 0: "native", + 1: "arterial", + 2: "portal", + 3: "delayed", +} + +CLINICAL_KEEP = [ + "age", + "gender_woman", + "etiology_mixed", + "etiology_HCV", + "etiology_HBV", + "etiology_alcoholic", + "etiology_NASH", + "etiology_cryptogenic", + "lesions_number", + "lesion1_diameter", + "lesion1_LIRADS", + "lesion2_diameter", + "lesion2_LIRADS", + "lesion3_diameter", + "lesion3_LIRADS", + "biopsy", + "lab_albumin", + "lab_creatinine", + "lab_bilirubin", + "lab_afp", + "lab_inr", + "lab_alt", + "tace_number", + "initial_LR_TR", + "cps", + "bclc", + "hap_score", + "mhap_2", + "albi_tae", + "6_12", + "6_12_score", + "nonv", +] + + +def rel(path: Path, root: Path) -> str: + if not path: + return "" + path = path if path.is_absolute() else path.absolute() + root = root if root.is_absolute() else root.absolute() + try: + return str(path.relative_to(root)) + except ValueError: + return str(path) + + +def ensure_dir(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + + +def link_file(src: Path, dst: Path, *, overwrite: bool = False) -> Path: + ensure_dir(dst.parent) + if dst.exists() or dst.is_symlink(): + if not overwrite: + return dst + dst.unlink() + os.symlink(src.resolve(), dst) + return dst + + +def write_csv(df: pd.DataFrame, path: Path) -> None: + ensure_dir(path.parent) + df.to_csv(path, index=False) + print(f"Wrote {path} ({len(df)} rows, {len(df.columns)} columns)") + + +def preprocess_msd(project_root: Path, overwrite: bool) -> pd.DataFrame: + src_root = project_root / "data" / "extracted" / "msd_liver" / "Task03_Liver" + out_root = project_root / "data" / "processed" / "msd_liver" + if not src_root.exists(): + print(f"Skip MSD: missing {src_root}") + return pd.DataFrame() + + rows = [] + dataset_json = json.loads((src_root / "dataset.json").read_text()) + for item in dataset_json["training"]: + image_src = (src_root / item["image"]).resolve() + label_src = (src_root / item["label"]).resolve() + if image_src.name.startswith("._") or not image_src.exists() or not label_src.exists(): + continue + case_id = image_src.name.replace(".nii.gz", "") + image_dst = link_file(image_src, out_root / "images" / image_src.name, overwrite=overwrite) + label_dst = link_file(label_src, out_root / "labels" / label_src.name, overwrite=overwrite) + rows.append( + { + "dataset": "msd_liver", + "patient_id": case_id, + "case_id": case_id, + "ct_path": rel(image_dst, project_root), + "label_path": rel(label_dst, project_root), + "liver_mask_label": 1, + "tumor_mask_label": 2, + "source": rel(src_root, project_root), + } + ) + + df = pd.DataFrame(rows).sort_values("case_id") + write_csv(df, out_root / "manifest.csv") + write_csv(df, project_root / "manifests" / "msd_liver_manifest.csv") + return df + + +def _load_nibabel(): + import nibabel as nib + + return nib + + +def _load_sitk(): + import SimpleITK as sitk + + return sitk + + +def derive_liver_mask(totalseg_path: Path, out_path: Path, overwrite: bool) -> Path: + if out_path.exists() and not overwrite: + return out_path + nib = _load_nibabel() + ensure_dir(out_path.parent) + img = nib.load(str(totalseg_path)) + data = np.asanyarray(img.dataobj) + liver = (data == 5).astype(np.uint8) + nib.save(nib.Nifti1Image(liver, img.affine, img.header), str(out_path)) + return out_path + + +def combine_tumor_nrrds(nrrd_paths: list[Path], out_path: Path, overwrite: bool) -> Path | None: + if not nrrd_paths: + return None + if out_path.exists() and not overwrite: + return out_path + sitk = _load_sitk() + ensure_dir(out_path.parent) + base = sitk.ReadImage(str(nrrd_paths[0])) + arr = sitk.GetArrayFromImage(base) > 0 + skipped = [] + for path in nrrd_paths[1:]: + img = sitk.ReadImage(str(path)) + if img.GetSize() != base.GetSize(): + skipped.append(path.name) + continue + arr |= sitk.GetArrayFromImage(img) > 0 + out = sitk.GetImageFromArray(arr.astype(np.uint8)) + out.CopyInformation(base) + sitk.WriteImage(out, str(out_path)) + if skipped: + print(f"Warning: skipped mismatched tumor masks for {out_path.name}: {', '.join(skipped)}") + return out_path + + +def choose_first(paths: list[Path | None]) -> Path | None: + for path in paths: + if path: + return path + return None + + +def preprocess_waw(project_root: Path, overwrite: bool, derive_liver: bool) -> pd.DataFrame: + raw_root = project_root / "data" / "raw" / "waw_tace" + src_root = project_root / "data" / "extracted" / "waw_tace" + out_root = project_root / "data" / "processed" / "waw_tace" + metadata_path = raw_root / "ct_hcc_metadata_v2.csv" + clinical_path = raw_root / "clinical_data_wawtace_v2_15_07_2024.xlsx" + if not metadata_path.exists() or not src_root.exists(): + print(f"Skip WAW-TACE: missing metadata or extracted data under {src_root}") + return pd.DataFrame() + + metadata = pd.read_csv(metadata_path) + clinical = pd.read_excel(clinical_path) + clinical = clinical.rename(columns={"PATPRI": "patient_id"}) + clinical["patient_id"] = clinical["patient_id"].astype(str) + clinical_by_patient = clinical.set_index("patient_id", drop=False) + + tumor_root = src_root / "tumor_masks_wawtace_v1_08_05_2024" + rows = [] + for patient_id, group in metadata.groupby(metadata["patient_id"].astype(str)): + row: dict[str, object] = { + "dataset": "waw_tace", + "patient_id": patient_id, + "case_id": patient_id, + } + phase_liver_paths: list[Path | None] = [] + phase_tumor_paths: list[Path | None] = [] + + for phase_num, phase_name in PHASES.items(): + phase_group = group[group["ct_phase"] == phase_num] + scan_src = src_root / patient_id / f"{patient_id}_{phase_num}_scan.nii.gz" + scan_dst = out_root / "images" / patient_id / scan_src.name + scan_exists = scan_src.exists() + if scan_exists: + link_file(scan_src, scan_dst, overwrite=overwrite) + row[f"ct_{phase_name}_path"] = rel(scan_dst, project_root) if scan_exists else "" + row[f"phase_available_{phase_name}"] = int(scan_exists) + + row[f"slice_thickness_{phase_name}"] = ( + float(phase_group["slice_thickness"].iloc[0]) if not phase_group.empty else np.nan + ) + row[f"tumor_count_{phase_name}"] = int(phase_group["tumor_count"].iloc[0]) if not phase_group.empty else np.nan + + organ_candidates = sorted( + list((src_root / patient_id).glob(f"{patient_id}_{phase_num}_total_segmentator*.nii.gz")) + + list((src_root / patient_id).glob(f"{patient_id}_{phase_num}_total_segmentator*.nii.nii.gz")) + ) + organ_src = organ_candidates[0] if organ_candidates else src_root / patient_id / f"{patient_id}_{phase_num}_total_segmentator.nii.nii.gz" + organ_dst = out_root / "organ_masks" / patient_id / f"{patient_id}_{phase_num}_totalsegmentator.nii.gz" + liver_path = None + if organ_src.exists(): + link_file(organ_src, organ_dst, overwrite=overwrite) + row[f"organ_mask_{phase_name}_path"] = rel(organ_dst, project_root) + if derive_liver: + liver_dst = out_root / "masks_liver" / patient_id / f"{patient_id}_{phase_num}_liver.nii.gz" + liver_path = derive_liver_mask(organ_src, liver_dst, overwrite) + row[f"liver_mask_{phase_name}_path"] = rel(liver_path, project_root) + else: + row[f"liver_mask_{phase_name}_path"] = "" + else: + row[f"organ_mask_{phase_name}_path"] = "" + row[f"liver_mask_{phase_name}_path"] = "" + phase_liver_paths.append(liver_path) + + nrrd_paths = sorted(tumor_root.glob(f"{patient_id}/{patient_id}_{phase_num}_*_tumor_seg.nrrd")) + tumor_path = combine_tumor_nrrds( + nrrd_paths, + out_root / "masks_tumor" / patient_id / f"{patient_id}_{phase_num}_tumor.nii.gz", + overwrite, + ) + row[f"tumor_mask_{phase_name}_path"] = rel(tumor_path, project_root) if tumor_path else "" + row[f"tumor_lesion_mask_count_{phase_name}"] = len(nrrd_paths) + phase_tumor_paths.append(tumor_path) + + row["liver_mask_path"] = rel(choose_first(phase_liver_paths), project_root) + row["tumor_mask_path"] = rel(choose_first(phase_tumor_paths), project_root) + + if patient_id in clinical_by_patient.index: + c = clinical_by_patient.loc[patient_id] + row["label_response"] = c.get("initial_tace_answer", np.nan) + row["label_progression"] = c.get("progression", np.nan) + row["time_pfs"] = c.get("progression_time", np.nan) + row["event_pfs"] = c.get("progression", np.nan) + row["time_os"] = c.get("survival_time", np.nan) + row["event_os"] = c.get("death", np.nan) + row["time_ttp"] = c.get("progression_time", np.nan) + row["event_ttp"] = c.get("progression", np.nan) + for col in CLINICAL_KEEP: + if col in c.index: + row[col] = c[col] + row[f"{col}_missing"] = int(pd.isna(c[col])) + rows.append(row) + + df = pd.DataFrame(rows).sort_values("patient_id") + clinical_out = out_root / "clinical.csv" + write_csv(clinical, clinical_out) + write_csv(df, out_root / "manifest.csv") + write_csv(df, project_root / "manifests" / "waw_tace_manifest.csv") + return df + + +def infer_hcc_phase(series_description: object, study_description: object) -> str: + text = f"{series_description or ''} {study_description or ''}".lower() + if "segmentation" in text: + return "seg" + if "pre" in text or "non" in text or "wo" in text: + return "native" + if "arter" in text or "art" in text: + return "arterial" + if "portal" in text or "venous" in text or "pv" in text: + return "portal" + if "delay" in text: + return "delayed" + if "3 phase" in text or "liver" in text: + return "multiphase_or_liver_protocol" + return "unknown" + + +def preprocess_hcc(project_root: Path) -> pd.DataFrame: + candidates = sorted((project_root / "data" / "raw" / "hcc_tace_seg").glob("manifest-*")) + if not candidates: + print("Skip HCC-TACE-Seg: no manifest-* directory found") + return pd.DataFrame() + manifest_root = candidates[-1] + metadata_path = manifest_root / "metadata.csv" + if not metadata_path.exists(): + print(f"Skip HCC-TACE-Seg: missing {metadata_path}") + return pd.DataFrame() + + metadata = pd.read_csv(metadata_path) + rows = [] + for _, rec in metadata.iterrows(): + series_dir = manifest_root / str(rec["File Location"]).strip("./") + phase = infer_hcc_phase(rec.get("Series Description"), rec.get("Study Description")) + rows.append( + { + "dataset": "hcc_tace_seg", + "patient_id": rec.get("Subject ID", ""), + "case_id": rec.get("Study UID", ""), + "series_uid": rec.get("Series UID", ""), + "study_date": rec.get("Study Date", ""), + "study_description": rec.get("Study Description", ""), + "series_description": rec.get("Series Description", ""), + "modality": rec.get("Modality", ""), + "sop_class_name": rec.get("SOP Class Name", ""), + "number_of_images": rec.get("Number of Images", ""), + "phase_guess": phase, + "dicom_series_dir": rel(series_dir, project_root) if series_dir.exists() else "", + "series_available": int(series_dir.exists()), + } + ) + df = pd.DataFrame(rows).sort_values(["patient_id", "study_date", "series_description"]) + out_root = project_root / "data" / "processed" / "hcc_tace_seg" + write_csv(metadata, out_root / "metadata.csv") + write_csv(df, out_root / "series_manifest.csv") + write_csv(df, project_root / "manifests" / "hcc_tace_seg_series_manifest.csv") + return df + + +def write_summary(project_root: Path, msd: pd.DataFrame, waw: pd.DataFrame, hcc: pd.DataFrame) -> None: + rows = [ + { + "dataset": "msd_liver", + "processed_rows": len(msd), + "notes": "MSD training image/label symlinks and manifest", + }, + { + "dataset": "waw_tace", + "processed_rows": len(waw), + "notes": "Patient-level multiphase CT manifest with clinical/outcome columns", + }, + { + "dataset": "hcc_tace_seg", + "processed_rows": len(hcc), + "notes": "DICOM series-level manifest; phase is inferred from descriptions", + }, + ] + write_csv(pd.DataFrame(rows), project_root / "manifests" / "preprocessing_summary.csv") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--project-root", type=Path, default=Path(__file__).resolve().parents[1]) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--skip-liver-derive", action="store_true", help="Do not derive binary liver masks from TotalSegmentator label 5.") + args = parser.parse_args() + + project_root = args.project_root.resolve() + msd = preprocess_msd(project_root, args.overwrite) + waw = preprocess_waw(project_root, args.overwrite, derive_liver=not args.skip_liver_derive) + hcc = preprocess_hcc(project_root) + write_summary(project_root, msd, waw, hcc) + + +if __name__ == "__main__": + main() diff --git a/CT/liver/scripts/run_benchmark.sh b/CT/liver/scripts/run_benchmark.sh new file mode 100644 index 0000000000000000000000000000000000000000..402577e21a7d56a3626b5a99a2ab83dbb4b39650 --- /dev/null +++ b/CT/liver/scripts/run_benchmark.sh @@ -0,0 +1,52 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT_DIR" + +export PYTHONPATH="$ROOT_DIR/src:${PYTHONPATH:-}" +CONDA_ENV="${CONDA_ENV:-ct}" +CONFIG="${1:-configs/benchmark.yaml}" + +run_py() { + conda run --no-capture-output -n "$CONDA_ENV" python "$@" +} + +mkdir -p logs experiments/benchmark + +echo "[benchmark] tabular clinical_logistic" +run_py -m liver_tace.tabular_benchmark --config "$CONFIG" --method clinical_logistic + +echo "[benchmark] tabular radiomics_svm" +run_py -m liver_tace.tabular_benchmark --config "$CONFIG" --method radiomics_svm + +echo "[benchmark] tabular radiomics_clinical_rf" +run_py -m liver_tace.tabular_benchmark --config "$CONFIG" --method radiomics_clinical_rf + +echo "[benchmark] deep CT-only resnet18" +run_py -m liver_tace.deep_outcome_baseline --config configs/benchmark/resnet18_ct_only.yaml + +echo "[benchmark] deep CT-only densenet121" +run_py -m liver_tace.deep_outcome_baseline --config configs/benchmark/densenet121_ct_only.yaml + +echo "[benchmark] SegResNet segmentation-only" +run_py -m liver_tace.train --config configs/benchmark/waw_seg_only.yaml +run_py -m liver_tace.evaluate \ + --config configs/benchmark/waw_seg_only.yaml \ + --checkpoint experiments/benchmark/segresnet_seg_only/checkpoints/best.pt \ + --output-dir experiments/benchmark/segresnet_seg_only + +echo "[benchmark] current full multitask with clean clinical columns" +run_py -m liver_tace.train --config configs/benchmark/full_multitask_clean.yaml +run_py -m liver_tace.evaluate \ + --config configs/benchmark/full_multitask_clean.yaml \ + --checkpoint experiments/benchmark/full_multitask/checkpoints/best.pt \ + --output-dir experiments/benchmark/full_multitask + +echo "[benchmark] VISTA3D/NV-Segment-CT external prediction evaluation" +run_py -m liver_tace.external_segmentation_benchmark --config "$CONFIG" --method vista3d_external --split val || true + +echo "[benchmark] aggregate" +run_py -m liver_tace.aggregate_benchmark --config "$CONFIG" --output-dir experiments/benchmark + +echo "[benchmark] done: experiments/benchmark/benchmark_summary.csv" diff --git a/CT/liver/scripts/run_hcc_external_benchmark.sh b/CT/liver/scripts/run_hcc_external_benchmark.sh new file mode 100644 index 0000000000000000000000000000000000000000..1d4db714c60cb079a11dddd5639e6b3430dde0a7 --- /dev/null +++ b/CT/liver/scripts/run_hcc_external_benchmark.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +set -euo pipefail + +CONFIG="${1:-configs/hcc_external_benchmark.yaml}" + +export PYTHONPATH="${PWD}/src:${PYTHONPATH:-}" +python -m liver_tace.hcc_external_benchmark --config "${CONFIG}" diff --git a/CT/liver/scripts/run_improved_training_eval.sh b/CT/liver/scripts/run_improved_training_eval.sh new file mode 100644 index 0000000000000000000000000000000000000000..dac4c0faf960b33ab63bc773e62a3a826a37025b --- /dev/null +++ b/CT/liver/scripts/run_improved_training_eval.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT_DIR" + +export PYTHONPATH="$ROOT_DIR/src:${PYTHONPATH:-}" +CONDA_ENV="${CONDA_ENV:-ct}" + +run_py() { + conda run --no-capture-output -n "$CONDA_ENV" python "$@" +} + +mkdir -p logs experiments/benchmark/full_multitask_improved experiments/hcc_external_improved + +echo "[improved] train full multitask model" +run_py -m liver_tace.train --config configs/benchmark/full_multitask_improved.yaml + +echo "[improved] evaluate WAW internal validation" +run_py -m liver_tace.evaluate \ + --config configs/benchmark/full_multitask_improved.yaml \ + --checkpoint experiments/benchmark/full_multitask_improved/checkpoints/best.pt \ + --output-dir experiments/benchmark/full_multitask_improved + +echo "[improved] evaluate HCC-TACE-Seg external validation" +run_py -m liver_tace.hcc_external_benchmark --config configs/hcc_external_improved.yaml + +echo "[improved] generate visualizations" +run_py -m liver_tace.visualize_results \ + --waw-config configs/benchmark/full_multitask_improved.yaml \ + --waw-checkpoint experiments/benchmark/full_multitask_improved/checkpoints/best.pt \ + --hcc-config configs/hcc_external_improved.yaml \ + --output-dir experiments/visualizations/improved_method \ + --n-cases 6 \ + --device auto + +echo "[improved] done" diff --git a/CT/liver/scripts/run_improved_v2_training_eval.sh b/CT/liver/scripts/run_improved_v2_training_eval.sh new file mode 100644 index 0000000000000000000000000000000000000000..6b47af0236623b8edc0662b4c0457063674c5ce2 --- /dev/null +++ b/CT/liver/scripts/run_improved_v2_training_eval.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT_DIR" + +export PYTHONPATH="$ROOT_DIR/src:${PYTHONPATH:-}" +CONDA_ENV="${CONDA_ENV:-ct}" + +run_py() { + conda run --no-capture-output -n "$CONDA_ENV" python "$@" +} + +mkdir -p logs experiments/benchmark/full_multitask_improved_v2 experiments/hcc_external_improved_v2 + +echo "[improved_v2] train full multitask model" +run_py -m liver_tace.train --config configs/benchmark/full_multitask_improved_v2.yaml + +echo "[improved_v2] evaluate WAW internal validation" +run_py -m liver_tace.evaluate \ + --config configs/benchmark/full_multitask_improved_v2.yaml \ + --checkpoint experiments/benchmark/full_multitask_improved_v2/checkpoints/best.pt \ + --output-dir experiments/benchmark/full_multitask_improved_v2 + +echo "[improved_v2] evaluate HCC-TACE-Seg external validation" +run_py -m liver_tace.hcc_external_benchmark --config configs/hcc_external_improved_v2.yaml + +echo "[improved_v2] generate visualizations" +run_py -m liver_tace.visualize_results \ + --waw-config configs/benchmark/full_multitask_improved_v2.yaml \ + --waw-checkpoint experiments/benchmark/full_multitask_improved_v2/checkpoints/best.pt \ + --hcc-config configs/hcc_external_improved_v2.yaml \ + --output-dir experiments/visualizations/improved_v2_method \ + --n-cases 6 \ + --device auto + +echo "[improved_v2] done" diff --git a/CT/liver/scripts/run_recent_benchmark.sh b/CT/liver/scripts/run_recent_benchmark.sh new file mode 100644 index 0000000000000000000000000000000000000000..2d50eaeb7657780f97bf7778a9be12f7e9e28dd9 --- /dev/null +++ b/CT/liver/scripts/run_recent_benchmark.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT_DIR" + +export PYTHONPATH="$ROOT_DIR/src:${PYTHONPATH:-}" +CONDA_ENV="${CONDA_ENV:-ct}" +CONFIG="${1:-configs/recent_benchmark.yaml}" + +run_py() { + conda run --no-capture-output -n "$CONDA_ENV" python "$@" +} + +mkdir -p logs experiments/recent_benchmark + +for method in \ + nnunet_resenc_2024 \ + transunet_media_2024 \ + segmamba_miccai_2024 \ + swin_umamba_miccai_2024 \ + hybridmamba_miccai_2025 +do + echo "[recent-benchmark] train/eval ${method}" + run_py -m liver_tace.recent_segmentation_benchmark --config "$CONFIG" --method "$method" +done + +echo "[recent-benchmark] aggregate" +run_py -m liver_tace.aggregate_benchmark --config "$CONFIG" --output-dir experiments/recent_benchmark + +echo "[recent-benchmark] done: experiments/recent_benchmark/benchmark_summary.csv" diff --git a/CT/liver/scripts/run_training_pipeline.sh b/CT/liver/scripts/run_training_pipeline.sh new file mode 100644 index 0000000000000000000000000000000000000000..c9de4a4535e2a8b196d7a229f856281f1f47af1b --- /dev/null +++ b/CT/liver/scripts/run_training_pipeline.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "${ROOT_DIR}" + +export PYTHONPATH="${ROOT_DIR}/src:${PYTHONPATH:-}" + +conda run --no-capture-output -n ct python -m liver_tace.train --config configs/msd_liver_seg.yaml +conda run --no-capture-output -n ct python -m liver_tace.train --config configs/waw_tace_multitask.yaml +conda run --no-capture-output -n ct python -m liver_tace.train --config configs/hcc_tace_external.yaml +conda run --no-capture-output -n ct python -m liver_tace.phase_ablation --config configs/waw_tace_multitask.yaml +conda run --no-capture-output -n ct python -m liver_tace.predict --config configs/waw_tace_multitask.yaml +conda run --no-capture-output -n ct python -m liver_tace.evaluate --config configs/msd_liver_seg.yaml --checkpoint experiments/checkpoints/msd_liver_seg/best.pt --output-dir experiments/results/eval_msd_liver_seg +conda run --no-capture-output -n ct python -m liver_tace.evaluate --config configs/waw_tace_multitask.yaml --checkpoint experiments/checkpoints/waw_tace_multitask/best.pt --output-dir experiments/results/eval_waw_tace_multitask +conda run --no-capture-output -n ct python -m liver_tace.evaluate --config configs/hcc_tace_external.yaml --checkpoint experiments/checkpoints/waw_tace_multitask/best.pt --output-dir experiments/results/eval_hcc_tace_external diff --git a/CT/liver/scripts/run_visualizations.sh b/CT/liver/scripts/run_visualizations.sh new file mode 100644 index 0000000000000000000000000000000000000000..8517ffcf2c0a1f86a4837ba7df44955e0af2736c --- /dev/null +++ b/CT/liver/scripts/run_visualizations.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +set -euo pipefail + +cd "$(dirname "$0")/.." +export PYTHONPATH="$PWD/src:${PYTHONPATH:-}" + +python -m liver_tace.visualize_results \ + --waw-config configs/benchmark/full_multitask_clean.yaml \ + --waw-checkpoint experiments/benchmark/full_multitask/checkpoints/best.pt \ + --hcc-config configs/hcc_external_benchmark.yaml \ + --output-dir experiments/visualizations/current_method \ + --n-cases 6 \ + --device cpu diff --git a/CT/liver/scripts/smoke_training_dataloader.py b/CT/liver/scripts/smoke_training_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..43df9ccfb09ba2dca460b856bc57883e8d556113 --- /dev/null +++ b/CT/liver/scripts/smoke_training_dataloader.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 +"""Smoke-test training-ready NPZ manifests with a PyTorch DataLoader.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +from torch.utils.data import DataLoader, Dataset + + +def center_crop_or_pad(array: np.ndarray, target_shape: tuple[int, ...]) -> np.ndarray: + result = np.zeros(target_shape, dtype=array.dtype) + src_slices = [] + dst_slices = [] + for src_size, dst_size in zip(array.shape, target_shape): + if src_size >= dst_size: + start = (src_size - dst_size) // 2 + src_slices.append(slice(start, start + dst_size)) + dst_slices.append(slice(0, dst_size)) + else: + start = (dst_size - src_size) // 2 + src_slices.append(slice(0, src_size)) + dst_slices.append(slice(start, start + src_size)) + result[tuple(dst_slices)] = array[tuple(src_slices)] + return result + + +class WawTaceDataset(Dataset): + def __init__(self, manifest_path: Path, patch_size: tuple[int, int, int]): + self.root = manifest_path.resolve().parents[1] + self.df = pd.read_csv(manifest_path) + self.patch_size = patch_size + + def __len__(self) -> int: + return len(self.df) + + def __getitem__(self, index: int) -> dict[str, torch.Tensor]: + row = self.df.iloc[index] + with np.load(self.root / row["npz_path"]) as data: + image = center_crop_or_pad(data["image"], (4, *self.patch_size)) + liver = center_crop_or_pad(data["liver_mask"], self.patch_size) + tumor = center_crop_or_pad(data["tumor_mask"], self.patch_size) + phase_available = data["phase_available"].astype(np.float32) + label_progression = np.asarray(row["label_progression"], dtype=np.float32) + return { + "image": torch.from_numpy(image.astype(np.float32)), + "liver_mask": torch.from_numpy(liver.astype(np.int64)), + "tumor_mask": torch.from_numpy(tumor.astype(np.int64)), + "phase_available": torch.from_numpy(phase_available), + "label_progression": torch.from_numpy(label_progression[None]), + } + + +class MsdLiverDataset(Dataset): + def __init__(self, manifest_path: Path, patch_size: tuple[int, int, int]): + self.root = manifest_path.resolve().parents[1] + self.df = pd.read_csv(manifest_path) + self.patch_size = patch_size + + def __len__(self) -> int: + return len(self.df) + + def __getitem__(self, index: int) -> dict[str, torch.Tensor]: + row = self.df.iloc[index] + with np.load(self.root / row["npz_path"]) as data: + image = center_crop_or_pad(data["image"], (1, *self.patch_size)) + label = center_crop_or_pad(data["label"], self.patch_size) + return { + "image": torch.from_numpy(image.astype(np.float32)), + "label": torch.from_numpy(label.astype(np.int64)), + } + + +class HccTaceSegDataset(MsdLiverDataset): + pass + + +def parse_patch_size(value: str) -> tuple[int, int, int]: + parts = tuple(int(x) for x in value.split(",")) + if len(parts) != 3: + raise ValueError("--patch-size must be z,y,x") + return parts + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dataset", choices=["waw", "msd", "hcc"], default="waw") + parser.add_argument("--batch-size", type=int, default=2) + parser.add_argument("--patch-size", default="96,128,128", help="Patch size in z,y,x order.") + args = parser.parse_args() + + patch_size = parse_patch_size(args.patch_size) + if args.dataset == "waw": + dataset: Dataset = WawTaceDataset(Path("manifests/waw_tace_training_manifest.csv"), patch_size) + elif args.dataset == "msd": + dataset = MsdLiverDataset(Path("manifests/msd_liver_training_manifest.csv"), patch_size) + else: + dataset = HccTaceSegDataset(Path("manifests/hcc_tace_seg_training_manifest.csv"), patch_size) + + loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=0) + batch = next(iter(loader)) + for key, value in batch.items(): + print(key, tuple(value.shape), value.dtype) + + +if __name__ == "__main__": + main() diff --git a/CT/lung/paper.pdf b/CT/lung/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3861e22c7200364791c579b1b990e941b464d8fe --- /dev/null +++ b/CT/lung/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e69b2056564b3109820c8dc7b6b1ac70a5b4ef0b09ba689e514c1585c0b3980 +size 22130499 diff --git "a/CT/lung2/\345\217\202\350\200\203\350\256\272\346\226\207.pdf" "b/CT/lung2/\345\217\202\350\200\203\350\256\272\346\226\207.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..5731f84ba3cfce1483c6dbc265506256efa5e11d --- /dev/null +++ "b/CT/lung2/\345\217\202\350\200\203\350\256\272\346\226\207.pdf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a099256400683e816b522885eea17d4f22233a1928c4729ccb187e2e6496dfe +size 12220137