Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
This work addresses a domain-specific challenge in medical imaging for cardiac pathology segmentation, with incremental improvements in method integration.
The authors tackled the problem of segmenting myocardial scar and edema from multi-sequence cardiac magnetic resonance images by proposing an automatic cascade framework with two neural networks and a denoising auto-encoder, achieving promising performance on the MyoPS2020 challenge dataset.
Multi-sequence of cardiac magnetic resonance (CMR) images can provide complementary information for myocardial pathology (scar and edema). However, it is still challenging to fuse these underlying information for pathology segmentation effectively. This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN). Specifically, the ASSN aims to segment the anatomical structure where the pathology may exist, and it can provide a spatial prior for the pathological region segmentation. In addition, we integrate a denoising auto-encoder (DAE) into the ASSN to generate segmentation results with plausible shapes. The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images. Experiments from the MyoPS2020 challenge dataset show that our framework can achieve promising performance for myocardial scar and edema segmentation.