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

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          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.

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          Deep Residual Learning for Image Recognition

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            U-Net: Convolutional Networks for Biomedical Image Segmentation

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              Densely Connected Convolutional Networks

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                Author and article information

                Contributors
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                02 March 2023
                2023
                : 10
                : 1153053
                Affiliations
                [1] 1Central Laboratory, Department of Ultrasound, Ningbo First Hospital , Ningbo, China
                [2] 2Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo, China
                Author notes

                Edited by: Guang Yang, Imperial College London, United Kingdom

                Reviewed by: Zhifan Gao, Sun Yat-sen University, China; Pu Zhaoxia, Zhejiang University, China

                *Correspondence: Huaying Hao haohuaying@ 123456nimte.ac.cn

                This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine

                †These authors have contributed equally to this work

                Article
                10.3389/fcvm.2023.1153053
                10018038
                36937939
                f8a4ee9a-68ce-4118-8042-ee64ce104a9c
                Copyright © 2023 Zhu, Zhang, Hao and Zhao.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 January 2023
                : 13 February 2023
                Page count
                Figures: 3, Tables: 3, Equations: 10, References: 35, Pages: 10, Words: 7033
                Categories
                Cardiovascular Medicine
                Original Research

                left atrial appendage,deep learning,segmentation,transesophageal echocardiography,latent space

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