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Adversarial-based latent space alignment network for left atrial appendage segmentation in transesophageal echocardiography images

Authors :
Xueli Zhu
Shengmin Zhang
Huaying Hao
Yitian Zhao
Source :
Frontiers in Cardiovascular Medicine, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Left atrial appendage (LAA) is a leading cause of atrial fibrillation and thrombosis in cardiovascular disease. Clinicians can rely on LAA occlusion (LAAO) to effectively prevent and treat ischaemic strokes attributed to the LAA. The correct selection of the LAAO is one of the most critical stages in the successful surgical process, which relies on the quantification of the anatomical structure of the LAA for successful intervention in LAAO. In this paper, we propose an adversarial-based latent space alignment framework for LAA segmentation in transesophageal echocardiography (TEE) images by introducing prior knowledge from the label. The proposed method consists of an LAA segmentation network, a label reconstruction network, and a latent space alignment loss. To be specific, we first employ ConvNeXt as the backbone of the segmentation and reconstruction network to enhance the feature extraction capability of the encoder. The label reconstruction network then encodes the prior shape features from the LAA labels to the latent space. The latent space alignment loss consists of the adversarial-based alignment and the contrast learning losses. It can motivate the segmentation network to learn the prior shape features of the labels, thus improving the accuracy of LAA edge segmentation. The proposed method was evaluated on a TEE dataset including 1,783 images and the experimental results showed that the proposed method outperformed other state-of-the-art LAA segmentation methods with Dice coefficient, AUC, ACC, G-mean, and Kappa of 0.831, 0.917, 0.989, 0.911, and 0.825, respectively.

Details

Language :
English
ISSN :
2297055X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Cardiovascular Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.0fcece58da534d038d05f70ca8686629
Document Type :
article
Full Text :
https://doi.org/10.3389/fcvm.2023.1153053