  Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology
information (scar and edema) to diagnose myocardial infarction. However,
automatic pathology segmentation can be challenging due to the difficulty of
effectively exploring the underlying information from the multi-sequence CMR
data. This paper aims to tackle the scar and edema segmentation from
multi-sequence CMR with a novel auto-weighted supervision framework, where the
interactions among different supervised layers are explored under a
task-specific objective using reinforcement learning. Furthermore, we design a
coarse-to-fine framework to boost the small myocardial pathology region
segmentation with shape prior knowledge. The coarse segmentation model
identifies the left ventricle myocardial structure as a shape prior, while the
fine segmentation model integrates a pixel-wise attention strategy with an
auto-weighted supervision model to learn and extract salient pathological
structures from the multi-sequence CMR data. Extensive experimental results on
a publicly available dataset from Myocardial pathology segmentation combining
multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising
performance compared with other state-of-the-art methods. Our method is
promising in advancing the myocardial pathology assessment on multi-sequence
CMR data. To motivate the community, we have made our code publicly available
via https://github.com/soleilssss/AWSnet/tree/master.
