  Pancreas segmentation in medical imaging data is of great significance for
clinical pancreas diagnostics and treatment. However, the large population
variations in the pancreas shape and volume cause enormous segmentation
difficulties, even for state-of-the-art algorithms utilizing
fully-convolutional neural networks (FCNs). Specifically, pancreas segmentation
suffers from the loss of spatial information in 2D methods, and the high
computational cost of 3D methods. To alleviate these problems, we propose a
probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture,
which fuses intra-slice information and inter-slice probabilistic maps into a
local 3D hybrid regularization scheme, which is followed by bi-directional
recurrent network optimization. The PBR-UNet method consists of an initial
estimation module for efficiently extracting pixel-level probabilistic maps and
a primary segmentation module for propagating hybrid information through a 2.5D
U-Net architecture. Specifically, local 3D information is inferred by combining
an input image with the probabilistic maps of the adjacent slices into
multichannel hybrid data, and then hierarchically aggregating the hybrid
information of the entire segmentation network. Besides, a bi-directional
recurrent optimization mechanism is developed to update the hybrid information
in both the forward and the backward directions. This allows the proposed
network to make full and optimal use of the local context information.
Quantitative and qualitative evaluation was performed on the NIH Pancreas-CT
dataset, and our proposed PBR-UNet method achieved better segmentation results
with less computational cost compared to other state-of-the-art methods.
