CLN Segmenter Dataset500 Fold0

--- license: cc-by-sa-4.0 tags: - nnunet - nnunetv2 - medical-imaging - segmentation - 3d-segmentation - ct - ldct - low-dose-ct - diagnostic-ct - lung - lung-cancer - tumor-segmentation - multi-institutional - pretrained library_name: nnunetv2 pipeline_tag: image-segmentation datasets: - MSD-Task06-Lung - NLSTseg language: - en --- # CLN-Segmenter — Dataset500 Unified Lung Lesion Pretrain (fold 0) A 3D U-Net (nnU-Net v2 `3d_fullres`) trained on **Dataset500_LungLesions**, the unified Stage 1 pretraining corpus combining MSD Task06 (diagnostic CT) and NLSTseg (low-dose screening CT) — 667 expert-annotated cases. Fold 0 of 5-fold cross-validation. Released as part of the CLN-Segmenter project at the Rasool Lab, Moffitt Cancer Center. This is the **v1 unified pretrain** intended as a starting point for downstream lung-lesion finetuning, especially when the target combines diagnostic and screening CT. ## Quick stats | | | |--|--| | **Architecture** | nnU-Net v2 `3d_fullres` (PlainConvUNet, 6 stages, features `[32, 64, 128, 256, 320, 320]`) | | **Training data** | Dataset500_LungLesions — 667 cases (533 train / 134 val for fold 0) | | **Composition** | 63 MSD Task06 (diagnostic CT, 9%) + 604 NLSTseg (LDCT, 91%) | | **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, ~6h 41m wall-time | | **Best EMA Pseudo Dice** (in-training) | **0.7658** (epoch ~960) | | **Mean Validation Dice** (per-case, sliding-window) | **0.6172** | | **Foreground IoU** | **0.5121** | | **Generalization** | No measurable overfitting — train/val loss curves overlap throughout | ## ⚠️ Two metrics, both honest — read this section The two Dice numbers reported above are computed differently and **disagree by ~0.15**. Both are correct; they answer different questions: ### `Best EMA Pseudo Dice = 0.7658` (in-training, voxel-pooled) Computed by nnU-Net every epoch on patches sampled from validation cases. Pools True Positives, False Positives, False Negatives across all val patches into one Dice. **Voxel-weighted**: large lesions dominate. This is the metric nnU-Net uses to select `checkpoint_best.pth`. ### `Mean Validation Dice = 0.6172` (sliding-window, per-case averaged) Computed *after* training by running full-volume sliding-wi...

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library nnunetv2 · Repo Lab-Rasool/CLN-Segmenter-Dataset500-fold0