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

Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation