Accurate identification of End-Diastolic (ED) and End-Systolic (ES) frames is key for cardiac function assessment through echocardiography. However, traditional methods face several limitations: they require extensive amounts of data, extensive annotations by medical experts, significant training resources, and often lack robustness. Addressing these challenges, we proposed an unsupervised and training-free method, our novel approach leverages unsupervised segmentation to enhance fault tolerance against segmentation inaccuracies. By identifying anchor points and analyzing directional deformation, we effectively reduce dependence on the accuracy of initial segmentation images and enhance fault tolerance, all while improving robustness. Tested on Echo-dynamic and CAMUS datasets, our method achieves comparable accuracy to learning-based models without their associated drawbacks. The code is available at https://github.com/MRUIL/DDSB
@article{arxiv.2403.12787,
title = {DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography},
author = {Zhenyu Bu and Yang Liu and Jiayu Huo and Jingjing Peng and Kaini Wang and Guangquan Zhou and Rachel Sparks and Prokar Dasgupta and Alejandro Granados and Sebastien Ourselin},
journal= {arXiv preprint arXiv:2403.12787},
year = {2024}
}