DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography
This addresses the need for robust phase detection in echocardiography without extensive data, annotations, or training resources, though it appears incremental as it builds on existing segmentation techniques.
The paper tackles the problem of accurately identifying End-Diastolic and End-Systolic frames in echocardiography for cardiac function assessment, proposing an unsupervised and training-free method that achieves comparable accuracy to learning-based models on Echo-dynamic and CAMUS datasets.
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