Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan

Abstract

Abstract Objective. Accurate segmentation of the pancreas from abdomen CT scans is highly desired for diagnosis and treatment follow-up of pancreatic diseases. However, the task is challenged by large anatomical variations, low soft-tissue contrast, and the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a new segmentation network and a semi-supervised learning framework to alleviate the lack of annotated images and improve the accuracy of segmentation. Approach. In this paper, we propose a novel graph-enhanced pancreas segmentation network (GEPS-Net), and incorporate it into a semi-supervised learning framework based on iterative uncertainty-guided pseudo-label refinement. Our GEPS-Net plugs a graph enhancement module on top of the CNN-based U-Net to focus on the spatial relationship information. For semi-supervised learning, we introduce an iterative uncertainty-guided refinement process to update pseudo labels by removing low-quality and incorrect regions. Main results. Our method was evaluated by a public dataset with four-fold cross-validation and achieved the DC of 84.22%, improving 5.78% compared to the baseline. Further, the overall performance of our proposed method was the best compared with other semi-supervised methods trained with only 6 or 12 labeled volumes. Significance. The proposed method improved the segmentation performance of the pancreas in CT images under the semi-supervised setting. It will assist doctors in early screening and making accurate diagnoses as well as adaptive radiotherapy.