--- license: cc-by-nc-4.0 task_categories: - image-segmentation language: - en tags: - medical - image - ct - abdomen - tumor - lesion - segmentation - detection - biometry pretty_name: 'mswal-lite' size_categories: - n<1K --- ## About This is a preprocessed redistribution of [MSWAL](https://github.com/haochen-MBZUAI/MSWAL-) ([HF](https://huggingface.co/datasets/zhaodongwu/MSWAL)), which is released under the `CC BY-NC 4.0` license. **Dataset summary:** 484 abdominal CT scans with 7-class whole-abdominal-lesion masks (gallstone, kidney stone, liver tumor, kidney tumor, pancreatic cancer, liver cyst, kidney cyst). **Contents of this repository:** - `Images/` — 484 files - `Masks/` — 484 files 📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can load the complete images and annotations from dataset configs. ## Relation to the source dataset | | | | --- | --- | | In the source | 694 single-hospital abdominal CT cases (MICCAI 2025), of which only the 484-case training split was ever uploaded — the 210-case test split listed in the upstream `dataset.json` points at `imagesTs/` files that do not exist on the hub | | Excluded here | nothing that exists upstream — all 484 published cases are mirrored | | **In this repo** | **484 `Images` + 484 `Masks`** | **All 484 published cases are included.** No format conversion was required: the source already ships `nii.gz`, and image voxel data is carried over unmodified. What is derived here is the flat `Images/` + `Masks/` layout with aligned basenames (the source's nnU-Net channel suffix `_0000` is stripped from image names), the mask re-headering onto the image grid, the `uint16` mask cast, and the RAS+ reorientation. The upstream test split is **not** withheld here — it was never published: the 210 `imagesTs/` entries in the source `dataset.json` have no corresponding files in the source repository. **Why `-Lite`?** The suffix marks this as a *derived* redistribution rather than a copy of the source. These are **preprocessed** volumes — every case has been format-converted where needed, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See [Preprocessing](#preprocessing) below for exactly what was changed. ## Preprocessing - Built from the official HF release `zhaodongwu/MSWAL` at pinned revision `62c286b05194bfad259de063878355766a6bed9d`. - `imagesTr/MSWAL_XXXX_0000.nii.gz` is renamed to `Images/MSWAL_XXXX.nii.gz` so image and mask basenames match; `labelsTr/` becomes `Masks/`. - The image NIfTI header is copied onto its mask, so each image/mask pair shares one grid and affine. - Masks are cast to `uint16`. - Images and masks are standardized to RAS+ orientation. 📝 The MedVision train/test split (338/146, seed 1024) is a re-split of the 484 published cases; if the upstream authors ever publish their 210-case test split, it will enter MedVision as a new dataset version rather than a rewrite of this one. ## Segmentation Labels ```python labels_map = { "1": "gallstone", "2": "kidney stone", "3": "liver tumor", "4": "kidney tumor", "5": "pancreatic cancer", "6": "liver cyst", "7": "kidney cyst" } ``` ## Landmarks ```python landmarks_map = { "P1": "most right/anterior/superior endpoint of the major axis", "P2": "most left/superior/inferior endpoint of the major axis", "P3": "most right/anterior/superior endpoint of the minor axis", "P4": "most left/superior/inferior endpoint of the minor axis" } ``` ## News - [9 Aug, 2026] Initial release. This dataset is integrated into 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can use these config names to load data in python: - `MSWAL_BoxSize_Task01_Axial_Test` - `MSWAL_BoxSize_Task01_Axial_Train` - `MSWAL_BoxSize_Task01_Coronal_Test` - `MSWAL_BoxSize_Task01_Coronal_Train` - `MSWAL_BoxSize_Task01_Sagittal_Test` - `MSWAL_BoxSize_Task01_Sagittal_Train` - `MSWAL_MaskSize_Task01_Axial_Test` - `MSWAL_MaskSize_Task01_Axial_Train` - `MSWAL_MaskSize_Task01_Coronal_Test` - `MSWAL_MaskSize_Task01_Coronal_Train` - `MSWAL_MaskSize_Task01_Sagittal_Test` - `MSWAL_MaskSize_Task01_Sagittal_Train` - `MSWAL_TumorLesionSize_Task01_Axial_Test` - `MSWAL_TumorLesionSize_Task01_Axial_Train` - `MSWAL_TumorLesionSize_Task01_Coronal_Test` - `MSWAL_TumorLesionSize_Task01_Coronal_Train` - `MSWAL_TumorLesionSize_Task01_Sagittal_Test` - `MSWAL_TumorLesionSize_Task01_Sagittal_Train` - `MSWAL_TumorLesionSize_Task02_Axial_Test` - `MSWAL_TumorLesionSize_Task02_Axial_Train` - `MSWAL_TumorLesionSize_Task02_Coronal_Test` - `MSWAL_TumorLesionSize_Task02_Coronal_Train` - `MSWAL_TumorLesionSize_Task02_Sagittal_Test` - `MSWAL_TumorLesionSize_Task02_Sagittal_Train` - `MSWAL_TumorLesionSize_Task03_Axial_Test` - `MSWAL_TumorLesionSize_Task03_Axial_Train` - `MSWAL_TumorLesionSize_Task03_Coronal_Test` - `MSWAL_TumorLesionSize_Task03_Coronal_Train` - `MSWAL_TumorLesionSize_Task03_Sagittal_Test` - `MSWAL_TumorLesionSize_Task03_Sagittal_Train` - `MSWAL_TumorLesionSize_Task04_Axial_Test` - `MSWAL_TumorLesionSize_Task04_Axial_Train` - `MSWAL_TumorLesionSize_Task04_Coronal_Test` - `MSWAL_TumorLesionSize_Task04_Coronal_Train` - `MSWAL_TumorLesionSize_Task04_Sagittal_Test` - `MSWAL_TumorLesionSize_Task04_Sagittal_Train` - `MSWAL_TumorLesionSize_Task05_Axial_Test` - `MSWAL_TumorLesionSize_Task05_Axial_Train` - `MSWAL_TumorLesionSize_Task05_Coronal_Test` - `MSWAL_TumorLesionSize_Task05_Coronal_Train` - `MSWAL_TumorLesionSize_Task05_Sagittal_Test` - `MSWAL_TumorLesionSize_Task05_Sagittal_Train` ## Data Usage Agreement By using the dataset, you agree to the terms as follow. - You must comply with the original `CC BY-NC 4.0` license terms of the source dataset. - You are recommended to refer to the source of this dataset in any publication: `https://huggingface.co/datasets/YongchengYAO/MSWAL-Lite` - You must cite the original publication(s): - https://arxiv.org/abs/2503.13560 ## Official Release For more information, please go to the official site: https://github.com/haochen-MBZUAI/MSWAL- ## Download from Huggingface ```python # python from huggingface_hub import snapshot_download snapshot_download(repo_id="YongchengYAO/MSWAL-Lite", repo_type='dataset', local_dir="/your/local/folder") ```