DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography

TL;DR

This paper introduces DDSB, an unsupervised, training-free method for phase detection in echocardiography that improves robustness and accuracy without requiring extensive data or annotations.

Contribution

The novel approach leverages unsupervised segmentation and deformation analysis to detect cardiac phases, reducing dependence on segmentation accuracy and training data.

Findings

Achieves comparable accuracy to supervised models

Enhances fault tolerance against segmentation errors

Operates without training, reducing resource requirements

Abstract

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