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 this https URL

通过无监督分割方法提高鲁棒性，减少依赖于初步分割图像的准确性，在保持与学习模型相当的准确性的同时，避免了传统方法对数据、注释、训练资源的大量需求。