YongchengYAO/MedVision

About

MedVision, a large-scale, multi-anatomy, multi-modality dataset for quantitative medical image analysis.

🌏 Project: [to be updated]

🧑🏻‍💻 Code: https://github.com/YongchengYAO/MedVision

🩻 Huggingface Dataset: YongchengYAO/MedVision

News

• [Oct 8, 2025] 🚀 Release MedVision dataset v1.0.0

TODO

• Add preprint, project page
• Add instructions on how to prepare the SKM-TEA and ToothFairy2 datasets
• Add tutorial on how to expand the dataset

Datasets

📝 The MedVision dataset consists of public medical images and quantitative annotations from this study. MRI: Magnetic Resonance Imaging; CT: Computed Tomography; PET: positron emission tomography; US: Ultrasound; b-box: bounding box; T/L: tumor/lesion size; A/D: angle/distance; HF: HuggingFace; GC: Grand-Challenge; * redistributed.

Dataset	Anatomy	Modality	Annotation	Availability	Source	# Sample (Train/Test)			Status
						b-box	T/L	A/D
AbdomenAtlas	abdomen	CT	b-box	open	HF	6.8 / 2.9M	0	0	✅
AbdomenCT-1K	abdomen	CT	b-box	open	Zenodo	0.7 / 0.3M	0	0	✅
ACDC	heart	MRI	b-box	open	HF*, others	9.5 / 4.8K	0	0	✅
AMOS22	abdomen	CT, MRI	b-box	open	Zenodo	0.8 / 0.3M	0	0	✅
autoPEI-III	whole body	CT, PET	b-box, T/L	open	HF*, others	22 / 9.7K	0.5 / 0.2K	0	✅
BCV15	abdomen	CT	b-box	open	HF*, Synapse	71 / 30K	0	0	✅
BraTS24	brain	MRI	b-box, T/L	open	HF*, Synapse	0.8 / 0.3M	7.9 / 3.1K	0	✅
CAMUS	heart	US	b-box	open	HF*, others	0.7 / 0.3M	0	0	✅
Ceph-Bio-400	head and neck	X-ray	b-box, A/D	open	HF*, others	0	0	5.3 / 2.3K	✅
CrossModDA	brain	MRI	b-box	open	HF*, Zenodo	3.0 / 1.0K	0	0	✅
FeTA24	fetal brain	MRI	b-box, A/D	registration	Synapse	34 / 15K	0	0.2 / 0.1K	✅
FLARE22	abdomen	CT	b-box	open	HF*, others	72 / 33K	0	0	✅
HNTSMRG24	head and neck	MRI	b-box, T/L	open	Zenodo	18 / 6.6K	1.0 / 0.4K	0	✅
ISLES24	brain	MRI	b-box	open	HF*, GC	7.3 / 2.5K	0	0	✅
KiPA22	kidney	CT	b-box, T/L	open	HF*, GC	26 / 11K	2.1 / 1.0K	0	✅
KiTS23	kidney	CT	b-box, T/L	open	HF*, GC	80 / 35K	5.9 / 2.6K	0	✅
MSD	multiple	CT, MRI	b-box, T/L	open	others	0.2 / 0.1M	5.3 / 2.2K	0	✅
OAIZIB-CM	knee	MRI	b-box	open	HF	0.5 / 0.2M	0	0	✅
SKM-TEA	knee	MRI	b-box	registration	others	0.2 / 0.1M	0	0	✅
ToothFairy2	tooth	CT	b-box	registration	others	1.0 / 0.4M	0	0	✅
TopCoW24	brain	CT, MRI	b-box	open	HF*, Zenodo	43 / 20K	0	0	✅
TotalSegmentator	multiple	CT, MRI	b-box	open	HF*, Zenodo	9.6 / 4.0M	0	0	✅
Total						22 / 9.2M	23 / 9.6K	5.6 / 2.4K

⚠️ For the following datasets, which do not allow redistribution, you need to apply for access from data owners, (optionally) upload to your private HF dataset repo, and set corresponding environment variables.

Dataset	Source	Host Platform	Env Var
FeTA24	https://www.synapse.org/Synapse:syn25649159/wiki/610007	Synapse	SYNAPSE_TOKEN
SKM-TEA	https://aimi.stanford.edu/datasets/skm-tea-knee-mri	Huggingface	MedVision_SKMTEA_HF_ID
ToothFairy2	https://ditto.ing.unimore.it/toothfairy2/	Huggingface	MedVision_ToothFairy2_HF_ID

📝 For SKM-TEA and ToothFairy2, you need to process the raw data and upload the preprocessed data to your private HF dataset repo. To use HF private dataset, you need to set HF_TOKEN and login with hf auth login --token $HF_TOKEN --add-to-git-credential

• Prepare SKM-TEA data: tutorial
• Prepare ToothFairy2 data: tutorial

Requirement

📝 Note: trust_remote_code is no longer supported in datasets>=4.0.0, install dataset with pip install datasets==3.6.0

Use

import os
from datasets import load_dataset

# Set data folder
os.environ["MedVision_DATA_DIR"] = <your/data/folder>

# Pick a dataset config name and split
config = <config-name>
split_name = "test", # use "test" for testing set config; use "train" for training set config 

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
    )

📝 List of config names in info/

Environment Variables

# Set where data will be saved, requires ~1T for the complete dataset
export MedVision_DATA_DIR=<your/data/folder>

# Force download and process raw images, default to "False"
export MedVision_FORCE_DOWNLOAD_DATA="False"

# Force install dataset codebase, default to "False"
export MedVision_FORCE_INSTALL_CODE="False"

Advanced Usage

The dataset codebase medvision_ds can be used to scale the dataset, including adding new annotation types and datasets.

🛠️ Install

pip install "git+https://huggingface.co/datasets/YongchengYAO/MedVision.git#subdirectory=src"
pip show medvision_ds

or

# First, install the benchmark codebase: medvision_bm
pip install "git+https://github.com/YongchengYAO/MedVision.git" 
 
# Install the dataset codebase: medvision_ds
pip install huggingface_hub
# NOTE: replace <local-data-folder>
python -c "from medvision_bm.utils import install_medvision_ds; install_medvision_ds(data_dir='<local-data-folder>')"

🧑🏻‍💻 Use utility functions for image processing

from medvision_ds.utils.data_conversion import (
    convert_nrrd_to_nifti,
    convert_mha_to_nifti,
    convert_nii_to_niigz,
    convert_bmp_to_niigz,
    copy_img_header_to_mask,
    reorient_niigz_RASplus_batch_inplace,
)

from medvision_ds.utils.preprocess_utils import (
    split_4d_nifti,
) 

👩🏼‍💻Examples of dataset scaling:

• Setup automatic data processing pipeline

  • Download preprocessed data from HF: medvision_ds/datasets/OAIZIB_CM/download.py
  • Download and processed data from source: medvision_ds/datasets/BraTS24/download_raw.py

• Prepare annotations

  • Generate b-box annotations from segmentation masks:

    • medvision_ds/datasets/BraTS24/preprocess_detection.py

  • Generate tumor/lesion size (TL) annotations from segmentation masks:

    • medvision_ds/datasets/BraTS24/preprocess_biometry.py

  • Generate angle/distance (AD) annotations from landmarks:

    • medvision_ds/datasets/Ceph_Biometrics_400/preprocess_biometry.py
    • medvision_ds/datasets/FeTA24/preprocess_biometry.py