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

• URL: http://arxiv.org/abs/2403.12787v1
• Date: Tue, 19 Mar 2024 14:51:01 GMT
• Title: DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography
• Authors: Zhenyu Bu, Yang Liu, Jiayu Huo, Jingjing Peng, Kaini Wang, Guangquan Zhou, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin,
• Abstract summary: We propose an unsupervised and training-free method to identify End-Diastolic (ED) and End-Systolic (ES) frames. By identifying anchor points and analyzing directional deformation, we effectively reduce dependence on the accuracy of initial segmentation images. Our method achieves comparable accuracy to learning-based models without their associated drawbacks.
• Score: 37.32413956117856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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