README.md

---
license: cc-by-4.0
tags:
- nnunet
- nnunetv2
- medical-imaging
- segmentation
- 3d-segmentation
- ct
- ldct
- low-dose-ct
- lung
- lung-cancer
- tumor-segmentation
- multi-institutional
library_name: nnunetv2
pipeline_tag: image-segmentation
datasets:
- NLSTseg
language:
- en
---

# CLN-Segmenter — NLSTseg Lung Lesion Segmentation (fold 0)

A 3D U-Net (nnU-Net v2 `3d_fullres`) trained on the **NLSTseg** dataset — pixel-level lung lesion annotations on low-dose screening CT (LDCT) from the National Lung Screening Trial. Fold 0 of 5-fold cross-validation. Released as part of the CLN-Segmenter project at the Rasool Lab, Moffitt Cancer Center.

This is a single-fold pretrain checkpoint, intended as a starting point for downstream lung-lesion segmentation work — not a clinical-grade tool.

## Quick stats

| | |
|--|--|
| **Architecture** | nnU-Net v2 `3d_fullres` (PlainConvUNet, 6 stages, features `[32, 64, 128, 256, 320, 320]`) |
| **Training data** | NLSTseg — 604 cases (1 excluded; 483 train / 121 val for fold 0) |
| **Modality** | Low-dose screening CT (LDCT), multi-institutional |
| **Loss** | Dice + Cross-Entropy (nnU-Net default), `batch_dice=True` |
| **Schedule** | 1000 epochs, polynomial LR decay 0.01 → 0, batch size 2, patch `[80, 192, 160]` |
| **Hardware** | 1× NVIDIA H100 80GB, ~7h wall-time |
| **Mean Validation Dice** (per-case, sliding-window) | **0.6123** |
| **Best EMA Pseudo Dice** (in-training proxy) | 0.7663 (epoch ~870) |
| **Generalization** | No measurable overfitting — train/val loss curves overlap throughout |

## Files in this repo

| File | Role |
|------|------|
| `checkpoint_best.pth` | Model weights — saved at the EMA Pseudo Dice peak (~epoch 870) |
| `nnUNetPlans.json` | Architecture spec + preprocessing plans. **Required** for inference. |
| `dataset.json` | Channel names, label names, file ending (nnU-Net v2 schema). **Required** for inference. |
| `dataset_fingerprint.json` | HU intensity stats from training data |
| `splits_final.json` | Train/val case ID splits for fold 0 (reproducibility) |
| `progress.png` | Training curves: loss, Pseudo Dice, epoch duration, learning rate |

## Training data and provenance

This model was trained **only on the publicly available NLSTseg dataset** (Chen et al. 2025, *Scientific Data*, CC-BY 4.0): pixel-level lung lesion annotations on top of NLST low-dose screening CT imagery. It contains 715 expert-annotated lesions across 605 patients (1 patient excluded — `nlst_0393` / patient 205714 — due to a CT/mask shape mismatch in the source files; see project changelog).

NLSTseg has key characteristics that make it complementary to diagnostic-CT datasets:

- **Multi-institutional**: 33 contributing institutions, 4 scanner brands (GE, Siemens, Philips, Toshiba)
- **Screening-cohort lesions**: smaller than typical diagnostic-CT tumors (median lesion volume **1.37 cm³**) — most caught at Stage IA
- **Multi-label source**: per-lesion integer labels (1–7) in the original masks; binarized to `{0, 1}` for this single-class training. The tumor-vs-nodule distinction (`labels_type` 1 vs 2 in the original `Label.xlsx`) is recoverable from the source if a future multi-class run is desired.
- **LDCT noise**: lower radiation dose than diagnostic CT; noisier images, often thicker slices

**No patient-identifiable or institutional data was used.** This checkpoint contains no information derived from any non-public source.

## Intended use

- **Pretrained starting point** for finetuning on related lung-lesion segmentation tasks, especially LDCT or screening-cohort data
- **Reference baseline** for nnU-Net default performance on NLSTseg's small-lesion, multi-institutional regime
- **Input to ensembling** with other folds (when 5-fold runs are available)

## How NOT to use it

- ❌ Not validated for clinical diagnosis or treatment decisions
- ❌ Not validated on diagnostic-CT cases (different intensity distributions, larger lesions) — see Limitations
- ❌ Single fold, not an ensemble — paper-grade results require all 5 folds
- ❌ Multi-lesion identity is collapsed in training labels; if your downstream task needs per-lesion instances, this checkpoint won't recover them directly

## How to use

### 1. Download the checkpoint and metadata

```python
from huggingface_hub import snapshot_download
local_dir = snapshot_download(repo_id="Lab-Rasool/CLN-Segmenter-NLSTseg-fold0")
print("Files at:", local_dir)
```

### 2. Set up an nnU-Net inference directory

nnU-Net expects a specific directory structure for results:

```
nnUNet_results/
└── Dataset503_NLSTseg/
    └── nnUNetTrainer__nnUNetPlans__3d_fullres/
        ├── dataset.json
        ├── plans.json                    (rename from nnUNetPlans.json)
        ├── dataset_fingerprint.json
        └── fold_0/
            ├── checkpoint_best.pth
            └── splits_final.json
```

You can build this with:

```bash
DST=/path/to/nnUNet_results/Dataset503_NLSTseg/nnUNetTrainer__nnUNetPlans__3d_fullres
mkdir -p $DST/fold_0
cp $local_dir/dataset.json              $DST/dataset.json
cp $local_dir/nnUNetPlans.json          $DST/plans.json
cp $local_dir/dataset_fingerprint.json  $DST/dataset_fingerprint.json
cp $local_dir/checkpoint_best.pth       $DST/fold_0/checkpoint_best.pth
cp $local_dir/splits_final.json         $DST/fold_0/splits_final.json
```

### 3. Run inference with nnU-Net

```bash
export nnUNet_results=/path/to/nnUNet_results
nnUNetv2_predict \
    -i /path/to/your/input_images \
    -o /path/to/output_predictions \
    -d 503 \
    -c 3d_fullres \
    -tr nnUNetTrainer \
    -p nnUNetPlans \
    -f 0 \
    -chk checkpoint_best.pth
```

Input images should be CT volumes named with the nnU-Net channel suffix: `<case_id>_0000.nii.gz`.

## Training procedure

- **Framework**: nnU-Net v2.7.0 (default trainer)
- **Preprocessing**: CT-specific normalization (HU clipping at the 0.5/99.5 percentiles of foreground voxels, then per-case z-score), resampling to target spacing `[1.25, 0.664, 0.664]` mm
- **Augmentation**: nnU-Net's default 3D augmentation pipeline (rotation, scaling, gamma, mirroring, gaussian noise/blur, low-resolution simulation)
- **Optimization**: SGD + Nesterov momentum (β=0.99), polynomial LR decay (initial LR 0.01)
- **Iterations**: fixed 250 per epoch (nnU-Net default; independent of dataset size)
- **Best-checkpoint mechanism**: nnU-Net automatically tracks EMA of validation Pseudo Dice and saves `checkpoint_best.pth` at the peak

## Evaluation

Two complementary Dice metrics, both honest, computed on the 121 fold-0 validation cases:

| Metric | Value | What it measures |
|--------|-------|------------------|
| **Mean Validation Dice** (per-case, sliding-window) | **0.6123** | Per-case Dice from full-volume `nnUNetv2_predict` inference, averaged across 121 val cases. **Case-weighted** — every scan counts equally regardless of tumor size. *This is the metric most papers report.* |
| **Best EMA Pseudo Dice** (in-training) | 0.7663 | Voxel-pooled Dice across validation patches during training. **Voxel-weighted** — large lesions dominate. Used by nnU-Net to select `checkpoint_best.pth`. |
| Pseudo Dice raw (jagged) range | 0.45–0.85 | (peak per-epoch readings during training) |
| Train/val loss gap (final epoch) | ~0 | No measurable overfitting throughout. |

The **0.15 gap** between Pseudo Dice (0.7663) and Mean Validation Dice (0.6123) is wider than the gap on uniform-tumor datasets like MSD Task06 (~0.10 gap there). NLSTseg has lesion volumes spanning 0.03 → 372 cm³ (median 1.37 cm³, long-tailed), so voxel-pooled Dice is dominated by the few large lesions while per-case Dice gives equal weight to many small-lesion cases that are individually harder. The voxel-pool vs case-average disagreement reflects this distribution honestly.

The training plot (`progress.png`) shows:

1. **Smooth Pseudo Dice climb** from 0 → 0.55 in the first ~50 epochs, then 0.55 → 0.77 over epochs 50–870. Slow continuous improvement throughout, with diminishing returns past epoch ~600.
2. **Train/val loss curves overlap nearly perfectly** end-to-end. With 483 training cases (10× MSD-only's 50), the model has enough data variety that it cannot memorize specifics. This translates into clean generalization — no overfitting to manage.

For comparisons against other methods, **cite the Mean Validation Dice (0.6123)**. Pseudo Dice is useful as an in-training monitoring signal but not for cross-method comparison.

Per-case validation results are available in `validation_summary.json` (Dice, IoU, TP/FP/FN counts per case).

The 0.6123 figure reflects the difficulty of small-lesion segmentation in heterogeneous, multi-institutional LDCT. It is the model's honest performance on its native validation distribution.

## Why this checkpoint matters

This is the **clean-generalization complement** to the MSD-only fold-0 checkpoint (`Lab-Rasool/CLN-Segmenter-MSD-fold0`). MSD shows what nnU-Net default does on a small (50 train / 13 val) single-institution diagnostic-CT corpus with large tumors → high Pseudo Dice (0.82) but with mild late-stage overfitting. NLSTseg shows the opposite end: ~10× more data (483 train / 121 val), multi-institutional LDCT, smaller lesions → lower raw Dice (0.77) but no overfitting.

For Stage 2 finetuning on a target domain, this checkpoint is the right choice when the target is screening / LDCT / multi-institutional / small-lesion. For diagnostic-CT-heavy targets, the MSD checkpoint or the unified `Dataset500_LungLesions` pretrain (when available) is the better starting point.

## Limitations

- **Single fold of 5-fold CV** — not an ensemble. Published-grade numbers require all 5 folds either averaged or ensembled at inference.
- **Trained on LDCT only** — performance on diagnostic CT is unknown and likely lower without finetuning (different HU distributions, less noise).
- **Small lesions dominate the training distribution** — performance on large primary tumors (e.g., >5 cm³) is not optimized for.
- **Multi-label → binary collapse**: per-lesion identity and tumor-vs-nodule distinction are lost in this checkpoint's outputs.
- **One source case excluded** (`nlst_0393` / patient 205714) due to source-data shape mismatch. Not a model issue, but worth knowing if you reproduce.
- **No clinical validation** — this is a research artifact, not a medical device.

## License

**CC-BY 4.0**, inherited from the NLSTseg source dataset license.

## Citation

If you use this model, please cite:

```bibtex
@article{isensee2021nnunet,
  title   = {nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author  = {Isensee, Fabian and Jaeger, Paul F and Kohl, Simon A A and Petersen, Jens and Maier-Hein, Klaus H},
  journal = {Nature Methods},
  volume  = {18},
  number  = {2},
  pages   = {203--211},
  year    = {2021}
}

@article{chen2025nlstseg,
  title   = {NLSTseg: A Pixel-level Lung Cancer Dataset Based on NLST LDCT Images},
  author  = {Chen, et al.},
  journal = {Scientific Data},
  year    = {2025},
  doi     = {10.1038/s41597-025-05742-x}
}

@article{nlst2011,
  title   = {Reduced lung-cancer mortality with low-dose computed tomographic screening},
  author  = {{The National Lung Screening Trial Research Team}},
  journal = {New England Journal of Medicine},
  year    = {2011},
  doi     = {10.1056/NEJMoa1102873}
}
```

## Project context

Part of **CLN-Segmenter** at the Rasool Lab, Moffitt Cancer Center: a two-stage approach for lung lesion segmentation that pretrains on public datasets (this is one component) and finetunes on internal data with domain-specific loss formulations.

- **Code**: https://github.com/lab-rasool/CLN-Segmenter
- **Lab**: https://huggingface.co/Lab-Rasool

Other models in this series:
- `Lab-Rasool/CLN-Segmenter-MSD-fold0` — single-dataset MSD Task06 POC (diagnostic CT, 63 expert cases, Dice 0.82)
- `Lab-Rasool/CLN-Segmenter-Dataset500-fold0` — unified MSD + NLSTseg pretrain (planned)