README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - image-segmentation
language:
  - en
tags:
  - medical
  - image
  - segmentation
  - MRI
  - knee
  - cartilage
pretty_name: oaizib-cm
size_categories:
  - n<1K

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.