README.md

---
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")
```