Keywords: Segmentation, Heart
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.
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