Summary 🌟

OAIZIB-CM: 507 knee MRIs and segmentation masks of 5 ROIs

Data

Source	link
Huggingface	main
	load_dataset-support
Zenodo	here
Google Drive	here

Huggingface Dataset Branch:
- main: The main branch contains the same files as those in Zenodo and Google Drive
- load_dataset-support: We added HF load_dataset() support in this branch (ref: intended usage 2)

About

This is the official release of OAIZIB-CM dataset

OAIZIB-CM is based on the OAIZIB dataset
- OAIZIB paper: Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative
In OAIZIB-CM, tibial cartilage is split into medial and lateral tibial cartilages.
OAIZIB-CM includes CLAIR-Knee-103R, consisting of
- a template image learned from 103 MR images of subjects without radiographic OA
- corresponding 5-ROI segmentation mask for cartilages and bones
- corresponding 20-ROI atlas for articular cartilages
It is compulsory to cite the paper if you use the dataset
- CartiMorph: A framework for automated knee articular cartilage morphometrics

Changelog 🔥

[22 Mar, 2025] Add HF load_dataset() support in the load_dataset-support branch.
[27 Feb, 2025] Add the template and atlas CLAIR-Knee-103R
[26 Feb, 2025] Update compulsory citation (CartiMorph) for the dataset
[15 Feb, 2025] Update file imagesTs/oaizib_454_0000.nii.gz
[14 Feb, 2025] Identify corrupted files: case 454

Files

Images & Labels

imagesTr: training images (#404)
labelsTr: training segmentation masks (#404)
imagesTs: testing images (#103)
labelsTs: testing segmentation masks (#103)

Data Split & Info

subInfo_train: list of training data
subInfo_test: list of testing data
kneeSideInfo: a file containing knee side information, used in CartiMorph Toolbox

Intended Usage

1. Download Files from the `main` or `load_dataset-support` Branch

#!/bin/bash
pip install --upgrade huggingface-hub[cli]
huggingface-cli login --token $HF_TOKEN

# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/OAIZIB-CM", repo_type='dataset', local_dir="/your/local/folder")

# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/OAIZIB-CM", repo_type='dataset', revision="load_dataset-support", local_dir="/your/local/folder")

2. Load `Dataset` or `IterableDataset` from the `load_dataset-support` Branch ‼️

>>> from datasets import load_dataset

# Load Dataset
>>> dataset_test = load_dataset("YongchengYAO/OAIZIB-CM", revision="load_dataset-support", trust_remote_code=True, split="test")
>>> type(dataset_test)
<class 'datasets.arrow_dataset.Dataset'>

# Convert Dataset to IterableDataset: use .to_iterable_dataset()
>>> iterdataset_test = dataset_test.to_iterable_dataset()
>>> type(iterdataset_test)
<class 'datasets.iterable_dataset.IterableDataset'>

# Load IteravleDataset: add streaming=True
>>> iterdataset_train = load_dataset("YongchengYAO/OAIZIB-CM", revision="load_dataset-support", trust_remote_code=True, streaming=True, split="train")
>>> type(iterdataset_train)
<class 'datasets.iterable_dataset.IterableDataset'>

🔥 Differences between Dataset and IterableDataset

Segmentation Labels

labels_map = {
    "1": "Femur",
    "2": "Femoral Cartilage",
    "3": "Tibia",
    "4": "Medial Tibial Cartilage",
    "5": "Lateral Tibial Cartilage",
}

Citations

The dataset originates from these projects:

CartiMorph: https://github.com/YongchengYAO/CartiMorph
CartiMorph Toolbox:
- https://github.com/YongchengYAO/CartiMorph-Toolbox
- https://github.com/YongchengYAO/CMT-AMAI24paper

@article{YAO2024103035,
title = {CartiMorph: A framework for automated knee articular cartilage morphometrics},
journal = {Medical Image Analysis},
author = {Yongcheng Yao and Junru Zhong and Liping Zhang and Sheheryar Khan and Weitian Chen},
volume = {91},
pages = {103035},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.103035}
}

@InProceedings{10.1007/978-3-031-82007-6_16,
author="Yao, Yongcheng
and Chen, Weitian",
editor="Wu, Shandong
and Shabestari, Behrouz
and Xing, Lei",
title="Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics",
booktitle="Applications of Medical Artificial Intelligence",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="162--172"
}

License

This dataset is released under the CC BY-NC 4.0 license.