Open AccessRU-Net for Heart Segmentation from CXR
Author(s) -
Yu Lyu,
Wei-Liang Huo,
Xiaolin Tian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1769/1/012015
Subject(s) - jaccard index , segmentation , net (polyhedron) , workload , residual , computer science , path (computing) , encoder , similarity (geometry) , artificial intelligence , pattern recognition (psychology) , image (mathematics) , algorithm , mathematics , computer network , geometry , operating system
Cardiovascular disease is one of the top causes of death in the world. In order to release heavy workload for doctor, automated segmentation methods using deep learning are proposed by researchers. Due to limitation of medical images, we proposed a novel model RU-Net based on the combination of U-Net and Residual Network for heart segmentation. We replaced Res path from direct skip connection from encoder to decoder. We use Jaccard similarity coefficient to compare the result of our method and U-Net with public dataset called Japanese Society of Radiological Technology (JSRT). The experiment result demonstrates the accuracy of our method.