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Datasets:
YongchengYAO
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MedVision

Tasks:
Image-Text-to-Text
Image Segmentation
Modalities:
Image
Languages:
English
Size:
10M<n<100M
ArXiv:
Tags:
medical
image
detection
measurement
angle
distance
License:
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metadata
license: cc-by-nc-4.0
task_categories:
  - image-text-to-text
  - image-segmentation
language:
  - en
tags:
  - medical
  - image
  - detection
  - measurement
  - angle
  - distance
pretty_name: medvision
size_categories:
  - 10M<n<100M

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

File structure: raw data will be automatically downloaded and processed, and our annotations are in each dataset folder

📝 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


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:


License: CC-BY-NC-4.0

This repository is released under CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).

✅ What you can do

  • Copy and redistribute the material in any medium or format.
  • Adapt, remix, transform, and build upon the material.
  • Use it privately or in non-commercial educational, research, or personal projects.

🚫 What you cannot do

  • Use the material for commercial purposes

📄 Requirement

Requirement Description
Attribution (BY) You must give appropriate credit, provide a link to this dataset, and indicate if changes were made.
NonCommercial (NC) You may not use the material for commercial purposes.
Indicate changes If you modify the work, you must note that it has been changed.