Abstract
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. MethodWe 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.
Highlights
Free-breathing cardiac magnetic resonance (CMR) cine imaging techniques have been developed for the evaluation of cardiac function [1,2,3,4,5]
Some commercial software could automatically segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool contours of end-diastole (ED) and end-systole (ES) frames, manual adjustment of the segmented contour is still required by an expert
The convergence speed of both models with improved generalized Dice (IGD) loss was faster than CE loss for the validation curve
Summary
Free-breathing cardiac magnetic resonance (CMR) cine imaging techniques have been developed for the evaluation of cardiac function [1,2,3,4,5]. It is an accurate and reproducible technique for chamber volume, myocardial mass, and stroke volume measurements [5]. Compared with breath-hold CMR cine imaging, it has a short acquisition time and eliminates the unnecessary breath-hold stage. It is beneficial for children and patients who are unable to hold their breath during data acquisition. A fully automatic segmentation method of ED and ES frames is necessary for improving the postprocessing efficiency of the free-breathing CMR cine imaging
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