文献类型：期刊文献

英文题名：Efficient Image Segmentation of Cardiac Conditions after Basketball Using a Deep Neural Network

作者：Ma, Jian[1];Li, Wenfa[2,3]

第一作者：Ma, Jian

通讯作者：Li, WF[1];Li, WF[2]

机构：[1]Univ Sci & Technol China, Sch Software Engn USTC, Suzhou 215123, Peoples R China;[2]Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China

第一机构：Univ Sci & Technol China, Sch Software Engn USTC, Suzhou 215123, Peoples R China

通讯机构：[1]corresponding author), Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2023

卷号：12

期号：2

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85146511539);WOS:【SCI-EXPANDED(收录号:WOS:000914572000001)】；

基金：This research was supported by the National Natural Science Foundation of China (No. 61972040).

语种：英文

外文关键词：deep neural network; attention module; encoding and decoding stage

摘要：The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images of the right ventricle. In this system, the right ventricle of the heart is segmented using an improved model called RAU-Net. The sensitivity and specificity of the network are enhanced by improving the loss function. We adopted an extended convolution rather than ordinary convolution to increase the receptive field of the network. In the network-sampling phase, we introduce an attention module to improve the accuracy of network segmentation. In the encoding and decoding stages, we also introduce three residual modules to solve the gradient explosion problem. The results of experiments are provided to show that the proposed algorithm exhibited better segmentation accuracy than an existing algorithm. Moreover, the algorithm can also be trained more rapidly and efficiently.