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

• Published in: BioMed research international Vol. 2019; no. 2019; pp. 1 - 12
• Main Authors: Zeng, Zhu, Miao, Yuehong, Wang, Lihui, Lei, Pinggui, Zhang, Yan, Yang, Fan, Xie, Hong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; Hindawi Limited
• Subjects: Adult; Algorithms; Automation; Blood; Cardiac function; Deep Learning; Diastole; Female; Fourier transforms; Function analysis; Heart; Heart - diagnostic imaging; Heart - physiology; Heart Ventricles - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted; Image processing; Image segmentation; Localization; Machine learning; Magnetic Resonance Imaging; Male; Myocardium; Neural networks; Neural Networks, Computer; NMR; Nuclear magnetic resonance; Resonance; Respiration; Signal processing; Systole; Teaching methods; Training; Ventricle; Ventricular Function - physiology
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2019/5636423

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.