  The irregular geometry and high inter-slice variability in computerized
tomography (CT) scans of the human pancreas make an accurate segmentation of
this crucial organ a challenging task for existing data-driven deep learning
methods. To address this problem, we present a novel model-driven stack-based
fully convolutional network with a sliding window fusion algorithm for pancreas
segmentation, termed MDS-Net. The MDS-Net's cost function includes a data
approximation term and a prior knowledge regularization term combined with a
stack scheme for capturing and fusing the two-dimensional (2D) and local
three-dimensional (3D) context information. Specifically, 3D CT scans are
divided into multiple stacks to capture the local spatial context feature. To
highlight the importance of single slices, the inter-slice relationships in the
stack data are also incorporated in the MDS-Net framework. For implementing
this proposed model-driven method, we create a stack-based U-Net architecture
and successfully derive its back-propagation procedure for end-to-end training.
Furthermore, a sliding window fusion algorithm is utilized to improve the
consistency of adjacent CT slices and intra-stack. Finally, extensive
quantitative assessments on the NIH Pancreas-CT dataset demonstrated higher
pancreatic segmentation accuracy and reliability of MDS-Net compared to other
state-of-the-art methods.
