Nnunet Breast Mri

--- license: apache-2.0 library_name: nnunetv2 pipeline_tag: image-segmentation tags: - medical-imaging - segmentation - nnu-net - breast - dce-mri - breast-cancer --- # nnU-Net v2 — Breast DCE-MRI Segmentation Weights Pretrained nnU-Net v2 weights for tissue segmentation on breast dynamic contrast-enhanced MRI (DCE-MRI). The accompanying inference/evaluation code is in [`code/`](./code); the online demo is the Space [`zhang0319/breast-mri-seg`](https://huggingface.co/spaces/zhang0319/breast-mri-seg). Four segmented classes: | Label value | Class | |---|---| | 1 | whole breast | | 2 | fibroglandular tissue (FGT) | | 3 | blood vessel | | 4 | lesion | ## Repository layout ``` tissue/ tissue-study models region/ region-study models └── Dataset0XX_.../nnUNetTrainer_750epochs__nnUNetResEncUNetLPlans__3d_fullres/ ├── fold_all/checkpoint_final.pth ├── plans.json ├── dataset.json └── dataset_fingerprint.json code/ full inference + evaluation package (nnunet-breast-inference) examples/ de-identified example cases (P0–P3 + ground-truth GT + reference predictions) ``` Model configuration (must match exactly when loading): `trainer = nnUNetTrainer_750epochs` · `plans = nnUNetResEncUNetLPlans` · `configuration = 3d_fullres` · `fold = all` ## Quick start (local, GPU recommended) ```bash # 1) Download this repo hf download zhang0319/nnunet-breast-mri --local-dir ./nnunet-breast-mri # 2) Install the code package cd nnunet-breast-mri/code pip install -e . # 3) Edit the config: point models_dir at the downloaded weights (which contain tissue/ and # region/), and source_dir at your raw DCE-MRI (one folder per patient: P0.nii.gz is the # pre-contrast image, P1/P2/... are post-contrast timepoints). cp config/inference_template.yaml config/my_site.yaml # 4) Run inference (you can skip extract-models when the weights are already unpacked) prepare-inputs --config config/my_site.yaml run-inference --config config/my_site.yaml format-outputs --config config/my_site.yaml build-breast-mask --config config/my_site.yaml ``` Each input channel is `[pre, post, post − pre]`. See `code/docs/Inference.md` for details. ## Intended use and limitations - **Intended use**: research, reproduction and teaching of breast DCE-MRI segmentation methods. - **Limitations**: trained on specific acquisition protocols / populations; performance may drop on other scanners, seque...

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library nnunetv2 · Repo zhang0319/nnunet-breast-mri