CLN Segmenter NLSTseg Fold0

--- 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 con...