| # ===================== ToothCanal-SDF default config ===================== | |
| paths: | |
| # On AutoDL, the large data disk is /root/autodl-tmp (system disk /root is small). | |
| hf_repo: "zgy0823/label" | |
| raw_dir: "/root/autodl-tmp/toothcanal/data/raw" | |
| proc_dir: "/root/autodl-tmp/toothcanal/data/processed" | |
| out_dir: "/root/autodl-tmp/toothcanal/outputs" | |
| # Drop the supplementary 21-30 TOOTH-label zip's contents here (see run notes). | |
| # Files named by case number, e.g. 21.nii.gz ... 30.nii.gz (or a per-case folder). | |
| extra_tooth_dir: "/root/autodl-tmp/toothcanal/extra_tooth_labels" | |
| # Zips whose name contains any of these substrings are NOT downloaded/used. | |
| skip_zip_substrings: ["36-40"] | |
| label_scheme: | |
| n_teeth: 28 # 28 tooth positions | |
| canal_lo: 1 # canal/pulp labels: 1..28 (inner) | |
| canal_hi: 28 | |
| body_lo: 29 # tooth-body labels: 29..56 (outer) | |
| body_hi: 56 | |
| pair_offset: 28 # instance i: canal=label i, body=label i+28 | |
| pair_mode: geometric # geometric = assign each canal to the body it sits inside | |
| # (robust to mis-numbered annotations; fixes 032/035/013/014). | |
| # set to 'offset' to restore legacy canal=i / body=i+28 pairing. | |
| preprocess: | |
| spacing: [0.4, 0.4, 0.4] # mm; whole-volume working resolution (memory friendly) | |
| clip_hu: [-1000, 3000] # intensity clip before normalization | |
| descriptor_erode_iter: 3 # erosion iters to build separable tooth-core descriptors | |
| # ---- canal cleaning ---- | |
| speck_min_voxels: 30 # remove connected components smaller than this (keeps real multi-canals) | |
| closing_radius: 0 # 0 = no morphological closing (avoids canal dilation at 0.25mm). | |
| # set to 1 only if your canals have many artifact-induced breaks. | |
| fill_holes: true | |
| split: | |
| # Cases 1-30 -> training (5-fold CV). Cases 31-35 -> held-out eval. | |
| # 36-40 has no usable data and is excluded. | |
| train_range: [1, 30] | |
| test_range: [31, 35] | |
| exclude_range: [36, 99] | |
| exclude_cases: [26] # 026 has tooth labels but NO canal labels (would teach | |
| # the model "no canal" for 28 teeth). add 19,20 too if | |
| # you want to drop the near-empty partial annotations. | |
| n_folds: 5 | |
| seed: 42 | |
| stage1: # coarse semantic seg: 0 bg / 1 tooth / 2 canal / 3 descriptor-core | |
| patch_size: [96, 96, 96] | |
| batch_size: 2 | |
| num_classes: 4 | |
| channels: [16, 32, 64, 128, 256] | |
| lr: 2.0e-4 | |
| max_epochs: 300 | |
| samples_per_volume: 4 | |
| val_interval: 10 | |
| use_descriptor: true # use class-3 cores for instance localization at inference | |
| min_core_voxels: 80 # drop tiny spurious cores (false detections) | |
| core_merge_mm: 3.0 # merge over-split cores whose centroids are <this apart | |
| # (fixes Stage-1 one-tooth-as-many). 0 disables merging. | |
| stage2: # per-tooth dual-SDF implicit reconstruction | |
| roi_mm: 24.0 # MATCHES the current stage2.pt (trained at 24mm). do NOT raise | |
| ckpt_name: stage2.pt # the current good model; retrain configs use a different name | |
| roi_vox: 96 # 24/96 = 0.25 mm. matches current stage2.pt | |
| roi_center: com # matches how the current checkpoint was trained | |
| latent_dim: 64 | |
| enc_channels: [32, 64, 128] # U-Net encoder (fine+coarse local features) | |
| feat_dim: 96 # per-point local feature width (was 64) | |
| mlp_hidden: 384 # wider decoder (was 256) | |
| mlp_layers: 6 # deeper decoder (was 5) | |
| encoder_latent: true # predict latent z = enc(ROI) (generalizes to new cases) | |
| points_per_tooth: 8192 # more query points (was 6144) | |
| canal_point_frac: 0.45 # fraction near the canal surface | |
| centerline_frac: 0.15 # extra fraction near the canal centerline | |
| canal_weight: 3.0 # up-weight the small canal (was 2.5) | |
| near_surface_ratio: 0.65 | |
| near_surface_sigma_mm: 0.5 | |
| sdf_clamp_mm: 2.0 | |
| occ_tau_mm: 0.3 | |
| loss_weights: | |
| sdf: 1.0 | |
| eikonal: 0.1 | |
| occ: 1.0 | |
| normal: 0.15 | |
| nest: 4.0 | |
| prior: 1.0e-3 | |
| centerline: 0.2 # softened clDice continuity (was 0.5, caused phantom blobs) | |
| smoothness: 0.5 # Duan-2021-style anti-phantom / volume control | |
| order_weight: 1.0 # weight of the hard SDF-ordering containment term | |
| lr: 4.0e-4 | |
| lr_latent: 1.0e-3 | |
| lr_min: 1.0e-5 # cosine anneal floor | |
| batch_teeth: 3 # local-feature grids use more memory | |
| max_epochs: 700 # deeper run for the bigger model | |
| margin_mm: 0.2 # containment margin | |
| augment: true # random 3D flips + 90-deg rotations (small-data regime) | |
| tto: # test-time optimization of the latent code | |
| steps: 500 # was 200 -- helps the long-tail cases (032/035) | |
| lr: 5.0e-3 | |
| infer: | |
| grid: 128 # was 96 -- finer marching cubes (smoother thin canals) | |
| mc_level: -0.2 # legacy fallback (used if the split knobs below are absent) | |
| mc_level_tooth: 0.0 # Tier 1: tooth meshed at GT level (0.0) -> removes -0.12 undersize | |
| mc_level_canal: -0.2 # canal body; scan -0.2/-0.25/-0.3 against signed_rvd (Tier 2) | |
| tooth_watertight_postprocess: true # tooth: keep largest component + fill holes (good) | |
| canal_watertight_postprocess: false # canal: do NOT fill -> preserves apex foramen + fine branches (Tier 2) | |
| pad_roi: true # pad the SDF grid border so MC closes boundary openings | |
| roi_source: "oracle" # "oracle"=GT instances (upper bound); "predicted"=Stage-1 drives ROIs | |
| use_tto: false # main results use NO test-time optimization (avoids GT leakage) | |
| canal_min_component_frac: 0.02 # 0.08->0.02 (Tier 2): keep small apical branches; rely on | |
| # largest (removes SDF bridge stubs; lower it if real thin | |
| # accessory canals are being discarded) | |
| eval: | |
| n_surface_samples: 30000 | |
| apex_mm: 3.0 # apical region size (mm) for root-tip-only canal metrics | |
| gt_wise_predicted: true # predicted eval: score 1 best prediction per GT tooth | |
| gt_roi_vox: 144 # GT meshed at 24/144=0.167mm, FIXED regardless of model, | |
| gt_grid: 144 # so 96- and 144-voxel models are compared on the SAME GT | |