Context Correlation Aware Network for Cardiac Segmentation

Automatically segmenting the anatomical structure of the heart from the cardiac magnetic resonance (CMR) images offers a great potential to augment the traditional healthcare strategy for the quantitative analysis of cardiac contractile function. Most of the existing CNN-based methods for cardiac segmentation tend to ignore the misalignment issues during the feature aggregation process and not fully use multi-scale context and contour information, which may lead to the unexpected misclassification caused by the falsely aligned contextual features and the discontinuity in the edge of segmentation maps. To resolve these issues, we proposed a context correlation aware network (CCA-Net). In CCA-Net, a volume correlation flow module was designed to align contour features and semantic features from adjacent levels, which offered the guidance to wrap low-resolution semantic features into high-resolution features. Besides, a residual gated squeeze module was utilized to explicitly model the boundaries and enhance the representations. Extensive experiments on the multi-sequence cardiac magnetic resonance segmentation challenge (MS-CMRSeg 2019) dataset and MICCAI challenge 2017 automatic cardiac diagnosis challenge (ACDC) dataset demonstrated that CCA-Net was superior to other state-of-the-art methods.