README.md

Dataset: OAIZIB-CM

Source	link
Google Drive	here
Huggingface	here

If you use our preprocessed data, please note that the manual segmentation annotations come from this work:

• Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (https://doi.org/10.1016/j.media.2018.11.009)

Download from Huggingface

#!/bin/bash
pip install 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")

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.xlsx: list of training data
• subInfo_test.xlsx: list of testing data
• kneeSideInfo.csv: a file containing knee side information, used in CartiMorph Toolbox

Paper

@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}
}

@misc{yao2024quantifyingkneecartilageshape,
      title={Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics}, 
      author={Yongcheng Yao and Weitian Chen},
      year={2024},
      eprint={2409.07361},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2409.07361}, 
}