Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing

Abstract

Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.

Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier

“…Nevertheless, as a few data for CAD were studied, the overfitting problem could happen. (Chendeb et al, 2022) [4] launched echocardiography video's semi-supervised segmentation by graph signal processing. GraphECV was the created model.…”

“…Step: 4 The flow of blood in the pulmonary circulation is determined. In pulmonary circulation, the flow of deoxygenated blood occurs and the blood flow is proportional to the number of weaker populations ( ) w .…”

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

“…Ouyang et al [8] 0.9270 Deng et al [9] 0.9164 Saeed et al [12] 0.9252 El rai et al [13] 0.9389 Puyol-Antón et al [14] 0.9350 Chen et al [15] 0.9440…”

“…It self-trains by leveraging one portion of the data to predict the other part and generate labels accurately. Another newly revised version of semi-supervised LV segmentation was introduced, which exploits graph signal processing [13]. In this work, instance segmentation and temporal, texture, and statistical feature extraction were required to represent the nodes, followed by graph sampling, where several labeled data were utilized to embed the graph.…”

An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography

“…The proposed method was evaluated using the EchoNet-Dynamic and CAMUS datasets, resulting in average Dice coefficients of 0.929 and 0.938, respectively, for the segmentation of the left ventricular endocardium. Additionally, based on these two datasets, El Rai et al ( 12 ) presented a new semi-supervised approach called GraphECV for the segmentation of the LV in echocardiography by using graph signal processing, respectively resulting in Dice coefficients of 0.936 and 0.940 with 1/2 labeled data for the left ventricular segmentation. Wei et al ( 13 ) used a co-learning mechanism to explore the mutual benefits of cardiac segmentation, therefore alleviating the noisy appearance.…”