Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
This work addresses the challenge of improving diagnostic accuracy for PDAC, a lethal cancer, by enhancing segmentation with multi-phase data, representing an incremental advance over single-phase methods.
The study tackled the problem of automatic segmentation of pancreatic ductal adenocarcinoma (PDAC) by integrating multi-phase imaging data, resulting in a significant improvement of up to 7.73% in Dice Similarity Coefficient (DSC) from 56.21% to 63.94%.
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.