Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics
AMAI’24 (MICCAI workshop) (in press)
CMT, a toolbox for knee MRI analysis, model training, and visualization.
- Joint Template-Learning and Registration Mode – CMT-reg
- CartiMorph Toolbox (CMT)
- models (both for segmentation and registration) for this work – can be loaded into CMT
- more models from the CMT models page
- For model evaluation and the training of other SoTA models:
git clone https://github.com/YongchengYAO/CMT-AMAI24paper.git
cd CMT-AMAI24paper
conda create --name CMT_AMAI24paper --file env.txt
- For model training in CMT, see the instructions for CMT
We compared the proposed CMT-reg with other template learning and/or registration models – Aladdin and LapIRN.
- Code for Aladdin training, inference, and evaluation (for reproducing the results in Tables 2 & 3)
- Code for LapIRN training, inference, and evaluation (for reproducing the results in Tables 2 & 3)
- Code for CMT evaluation (for reproducing the results in Table 3)
- Training, inference, and evaluation of CMT-reg are implemented in CMT, set these parameters in CMT:
- Cropped Image Size: 64, 128, 128
- Training Epoch: 2000
- Network Width: x3
- Loss: MSE+LNCC
This is the data used for reproducing Tables 2 & 3.
Data for this repo for model training, inference, and evaluation
# data folder structure
├── Code
├── Aladdin
├── Model
├── LapIRN
├── Model
├── Data
├── Aladdin
├── CMT_data4AMAI
├── LapIRN
- How to use files in the
Data
folder?- clone this repo:
CMT-AMAI24paper
- put the
Data
folder underCMT-AMAI24paper/
- clone this repo:
- How to use files in the
Code
folder?- clone this repo:
CMT-AMAI24paper
- put corresponding
Model
folders toCMT-AMAI24paper/Code/Aladdin/
CMT-AMAI24paper/Code/LapIRN/
- clone this repo:
- MR Image: OAI
- Annotation: OAI-ZIB
Data Information: here (link CMT-ID to OAI-SubjectID)
This is the data used for training CMT-reg and nnUNet in CMT
If you use the processed 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)
(conference proceedings in press)
@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},
}
The training, inference, and evaluation code for Aladdin and LapIRN are adapted from these GitHub repos:
- Aladdin: https://github.com/uncbiag/Aladdin
- LapIRN: https://github.com/cwmok/LapIRN
CMT is based on CartiMorph: https://github.com/YongchengYAO/CartiMorph
@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}
}