  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.
