  Cardiac MRI segmentation plays a crucial role in clinical diagnosis for
evaluating personalized cardiac performance parameters. Due to the indistinct
boundaries and heterogeneous intensity distributions in the cardiac MRI, most
existing methods still suffer from two aspects of challenges: inter-class
indistinction and intra-class inconsistency. To tackle these two problems, we
propose a novel method to exploit the directional feature maps, which can
simultaneously strengthen the differences between classes and the similarities
within classes. Specifically, we perform cardiac segmentation and learn a
direction field pointing away from the nearest cardiac tissue boundary to each
pixel via a direction field (DF) module. Based on the learned direction field,
we then propose a feature rectification and fusion (FRF) module to improve the
original segmentation features, and obtain the final segmentation. The proposed
modules are simple yet effective and can be flexibly added to any existing
segmentation network without excessively increasing time and space complexity.
We evaluate the proposed method on the 2017 MICCAI Automated Cardiac Diagnosis
Challenge (ACDC) dataset and a large-scale self-collected dataset, showing good
segmentation performance and robust generalization ability of the proposed
method.
