  Myocardial pathology segmentation (MyoPS) can be a prerequisite for the
accurate diagnosis and treatment planning of myocardial infarction. However,
achieving this segmentation is challenging, mainly due to the inadequate and
indistinct information from an image. In this work, we develop an end-to-end
deep neural network, referred to as MyoPS-Net, to flexibly combine
five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract
precise and adequate information, we design an effective yet flexible
architecture to extract and fuse cross-modal features. This architecture can
tackle different numbers of CMR images and complex combinations of modalities,
with output branches targeting specific pathologies. To impose anatomical
knowledge on the segmentation results, we first propose a module to regularize
myocardium consistency and localize the pathologies, and then introduce an
inclusiveness loss to utilize relations between myocardial scars and edema. We
evaluated the proposed MyoPS-Net on two datasets, i.e., a private one
consisting of 50 paired multi-sequence CMR images and a public one from
MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could
achieve state-of-the-art performance in various scenarios. Note that in
practical clinics, the subjects may not have full sequences, such as missing
LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to
investigate the performance of the proposed method in dealing with such complex
combinations of different CMR sequences. Results proved the superiority and
generalizability of MyoPS-Net, and more importantly, indicated a practical
clinical application.
