  Cardiac segmentation of atriums, ventricles, and myocardium in computed
tomography (CT) images is an important first-line task for presymptomatic
cardiovascular disease diagnosis. In several recent studies, deep learning
models have shown significant breakthroughs in medical image segmentation
tasks. Unlike other organs such as the lungs and liver, the cardiac organ
consists of multiple substructures, i.e., ventricles, atriums, aortas,
arteries, veins, and myocardium. These cardiac substructures are proximate to
each other and have indiscernible boundaries (i.e., homogeneous intensity
values), making it difficult for the segmentation network focus on the
boundaries between the substructures. In this paper, to improve the
segmentation accuracy between proximate organs, we introduce a novel model to
exploit shape and boundary-aware features. We primarily propose a shape-aware
attention module, that exploits distance regression, which can guide the model
to focus on the edges between substructures so that it can outperform the
conventional contour-based attention method. In the experiments, we used the
Multi-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images
for training and validation, and 40 CT cardiac images for testing. The
experimental results show that the proposed network produces more accurate
results than state-of-the-art networks by improving the Dice similarity
coefficient score by 4.97%. Our proposed shape-aware contour attention
mechanism demonstrates that distance transformation and boundary features
improve the actual attention map to strengthen the responses in the boundary
area. Moreover, our proposed method significantly reduces the false-positive
responses of the final output, resulting in accurate segmentation.
