PCA-Net: a heart segmentation model based on the meta-learning method  

作　　者：YANG Mengzhu ZHU Dong DONG Hao HU Shunbo WANG Yongfang 

机构地区：[1]School of Information Science and Engineering,Linyi University,Linyi 276000,China

出　　处：《Optoelectronics Letters》2024年第11期697-704,共8页光电子快报（英文版）

基　　金：supported by the Shandong Provincial Natural Science Foundation(Nos.ZR2019PF005,ZR2021MF115 and ZR2023MF062);the Introduction and Cultivation Program for Young Innovative Talents of Universities in Shandong(No.2021QCYY003)。

摘　　要：In order to effectively prevent and treat heart-based diseases,the study of precise segmentation of heart parts is particularly important.The heart is divided into four parts:the left and right ventricles and the left and right atria,and the left main trunk is more important,thus the left ventricular muscle(LV-MYO),which is located in the middle part of the heart,has become the object of many researches.Deep learning medical image segmentation methods become the main means of image analysis and processing at present,but the deep learning methods based on traditional convolutional neural network(CNN)are not suitable for segmenting organs with few labels and few samples like the heart,while the meta-learning methods are able to solve the above problems and achieve better results in the direction of heart segmentation.Since the LV-MYO is wrapped in the left ventricular blood pool(LV-BP),this paper proposes a new model for heart segmentation:principle component analysis network(PCA-Net).Specifically,we redesign the coding structure of Q-Net and make improvements in threshold extraction.Experimental results confirm that PCA-Net effectively improves the accuracy of segmenting LV-MYO and LV-BP sites on the CMR dataset,and is validated on another publicly available dataset,ABD,where the results outperform other state-of-the-art(SOTA)methods.

关 键 词：ORGANS VENTRICULAR DISEASES 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] R318[医药卫生—生物医学工程]