  Accurate and robust whole heart substructure segmentation is crucial in
developing clinical applications, such as computer-aided diagnosis and
computer-aided surgery. However, segmentation of different heart substructures
is challenging because of inadequate edge or boundary information, the
complexity of the background and texture, and the diversity in different
substructures' sizes and shapes. This article proposes a framework for
multi-class whole heart segmentation employing non-rigid registration-based
probabilistic atlas incorporating the Bayesian framework. We also propose a
non-rigid registration pipeline utilizing a multi-resolution strategy for
obtaining the highest attainable mutual information between the moving and
fixed images. We further incorporate non-rigid registration into the
expectation-maximization algorithm and implement different deep convolutional
neural network-based encoder-decoder networks for ablation studies. All the
extensive experiments are conducted utilizing the publicly available dataset
for the whole heart segmentation containing 20 MRI and 20 CT cardiac images.
The proposed approach exhibits an encouraging achievement, yielding a mean
volume overlapping error of 14.5 % for CT scans exceeding the state-of-the-art
results by a margin of 1.3 % in terms of the same metric. As the proposed
approach provides better-results to delineate the different substructures of
the heart, it can be a medical diagnostic aiding tool for helping experts with
quicker and more accurate results.
