  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers
with an overall five-year survival rate of 8%. Due to subtle texture changes of
PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of
pancreatic disease. In this study, we aim at enhancing PDAC automatic
segmentation by integrating multi-phase information (i.e., arterial phase and
venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully
convolution neural network which effectively integrates information from
different phases. The proposed approach consists of a dual path network where
the two parallel streams are interconnected with hyper-connections for
intensive information exchange. Additionally, a pairing loss is added to
encourage the commonality between high-level feature representations of
different phases. Compared to prior arts which use single phase data, HPN
reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms
of DSC.
