A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging

BioMed Research International

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[1] Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China, gmcah.com;Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China, gmc.edu.cn;Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China, gmc.edu.cn;School of Biology & Engineering, Guizhou Medical University, Guiyang 550025, China, gmc.edu.cn;Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang 550025, China, gzu.edu.cn;

DOI  :  10.1155/2019/5636423

来源: publisher

【 摘 要 】

Objectives. The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis. Method. We proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting. Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing. Results. The average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV). The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV). Conclusions. The proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.

【 授权许可】

Unknown