A dual branch and fine-grained enhancement network for pancreatic tumor segmentation in contrast enhanced CT images

Abstract

Segmentation of pancreatic tumors in CT images is important for clinical diagnosis and treatment, but it faces challenges of small size, low contrast, and large position difference. To address these issues, the abnormal pancreas is first segmented based on a dual branch coding network (DB-Net) using a coarse-to-fine segmentation strategy. In the encoder part, one branch extracts the semantic features of the pancreas and its surroundings, and the other branch captures the complex pancreas through wide-channel convolution and few down-sampling operations. An aggregation layer is used to fuse the different feature maps obtained by the two branches, and a U-Net decoder is used to segment the abnormal pancreas in CT images with pancreatic tumors. DB-Net is further trained to obtain the accurate pancreatic segmentation. Then, pancreatic tumors are segmented in the pancreas based on the fine-grained enhancement network (FE-Net). The FE-Net integrates a contrast enhancement block with a reverse attention block to extract detailed features and excavate effective information from the feature maps of the encoder and decoder to segment pancreatic tumors. In order to segment the tumor more accurately, the pancreatic tumor is segmented in the cropped pancreas. Experiments on 116 contrast-enhanced abdominal CT volumes of pancreatic cancer and 42 contrast-enhanced abdominal CT volumes of normal pancreas verify the effectiveness of the proposed framework in pancreatic tumor segmentation by using the two-fold cross-validation strategy. Compared to state-of-the-art deep learning segmentation network, the proposed method can achieve better segmentation of pancreas and pancreatic tumors.