Automatic Right Ventricular Segmentation of Cardiac Cine Magnetic Resonance Images Based on a Novel Multi-atlas Two-Stage U-net

Motivation: Right ventricular(RV) segmentation is of great significance for the clinical diagnosis of heart diseases. However, due to the complex structure, RV segmentation is still challenging. Goal(s): Fully automatic and accurate segmentation of the right ventricle. Approach: A new deep atlas network that combines atlas prior knowledge with Deformable Multi-scale Two-Stage U-net(DMTSU-net) is proposed to extract and fuse multi-scale RV features in Cine Cardiac Magnetic Resonance (CCMR) images. Results: Compare with 8 classical methods, the segmentation results of DMTSU-net are mostly close to the gold standard and significantly correlate with it on all evaluation indices in 15 testing datasets. Impact: The proposed framework integrates prior information of atlases into a deep neural network to achieve accurate segmentation, which is promising for clinical heart disease diagnosis.