Verse Vertebrae Segmentation Nnunet

--- tags: - medical - segmentation - vertebrae - nnunet - unet - ct license: apache-2.0 pipeline_tag: image-segmentation --- # Spinal Vertebrae Segmentation — nnUNet Model Pre-trained nnUNetv2 model for automatic segmentation of 25 vertebrae classes (C1–C7, T1–T12, L1–L6, T13) from CT scans. Trained on the [VerSe 2020](https://github.com/anjany/verse) dataset using a Residual Encoder U-Net (ResEncUNet-M) architecture in 3D low-resolution configuration. **Test set performance:** Mean Dice 0.729, Mean IoU 0.672 (73 cases). Outperforms general medical segmentation models on the same dataset. (specifically, TotalSegmentator with Dice 0.708). --- ## Model Details | Property | Value | |----------|-------| | Framework | nnUNetv2 | | Configuration | 3d_lowres | | Planner | nnUNetResEncUNetMPlans | | Folds | 0, 1 | | Input | CT (NIfTI, `.nii.gz`) | | Labels | 26 classes (background + C1–C7, T1–T13, L1–L6) | --- ## Download ### Option 1: Hugging Face CLI ```bash pip install huggingface_hub huggingface-cli download lukatman/verse-vertebrae-segmentation-nnunet --local-dir ./downloaded_model ``` ### Option 2: Git ```bash git clone https://huggingface.co/lukatman/verse-vertebrae-segmentation-nnunet ``` --- ## Usage with nnUNet ### 1. Install nnUNetv2 ```bash pip install nnunetv2 ``` Set environment variables: ```bash export nnUNet_raw="/path/to/nnUNet_raw" export nnUNet_preprocessed="/path/to/nnUNet_preprocessed" export nnUNet_results="/path/to/nnUNet_results" ``` ### 2. Place the model in nnUNet_results The downloaded folder contains `nnUNet_results/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_lowres/`. Copy it into your nnUNet results directory under the dataset name: ```bash # After downloading, create the dataset folder and copy the model mkdir -p $nnUNet_results/Dataset001_VerSe cp -r downloaded_model/nnUNet_results/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_lowres \ $nnUNet_results/Dataset001_VerSe/ ``` ### 3. Run inference ```bash nnUNetv2_predict -d 001 -c 3d_lowres -f 0 -p nnUNetResEncUNetMPlans \ -i /path/to/input/ct/scans \ -o /path/to/output/predictions ``` Use `-f 1` for fold 1, or run both with `-f 0 1` for ensemble. --- ## Label Mapping | Label | Vertebra | |-------|----------| | 1–7 | C1–C7 (cervical) | | 8–19 | T1–T12 (thoracic) | | 20–25 | L1–L6 (lumbar) | | 26 | T13 (rare variant) | --- ## Project & Citation - **Git...

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