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Updated: Jan 20, 2026
Cardiac Magnetic Resonance Imaging at 7 Tesla
Published on: January 6, 2019
A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging
Fan Yang1,2, Yan Zhang3, Pinggui Lei3
1Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang 550025, China.
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.
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