DRAN: Deep recurrent adversarial network for automated pancreassegmentation

Abstract

Automated pancreas segmentation in abdominal computed tomography (CT) scans is of high clinical relevance (i.e. pancreas cancer diagnosis and prognosis), but extremely difficult because the pancreas is a soft, small, and flexible abdominal organ with high anatomical variability, which causes the previous segmentation methods to result in low precision. In this study, the authors present a new deep recurrent adversarial network (DRAN) to tackle this challenge. DRAN contains three steps: (i) preserving global resolution of CT scans and modifying the receptive field of kernel adaptively through a dilated convolution autoencoder module; (ii) modelling contextual spatial correlation between neighbouring CT scan patches benefits from a specially designed local long short-term memory module; and (iii) improving the performance and generalisation by leveraging an adversarial module, which can constrain the spatial smoothness consistency between continuous CT scans based on the long-range spatial interaction. The system is evaluated on a dataset of 80 manually segmented CT volumes, using four-fold cross-validation. Its performance surpasses other state-of-the-art methods, with the Dice similarity coefficient of 89.87 ± 3.17 % and pixel-wise accuracy of 95.85 ± 3.04 % . Also, they perform a qualitative evaluation by an expert further revealing the effectiveness and potential of their DRAN as a clinical segmentation tool.