Automatic pancreas segmentation in abdominal CT image with contrast enhancement block

Automatic pancreas segmentation in abdominal CT images plays an important role in clinical applications. It can provide doctors with quantitative and qualitative information. Due to the small size, unclear edges, and the high anatomical differences between patients, it is a challenging task to accurately segment the pancreas with diseases. In this paper, we propose a new method to automatically segment the pancreas in abdominal CT images. First, we propose a contrast enhancement block. The block generates edge information and uses gating mechanism to enhance edge details of the pancreas. Second, we leverage a reverse attention block. This block utilizes the decoder feature map to guide the network to mine complementary discriminative regions. The proposed method is trained on 63 3D CT images, validated on 15 3D CT images, and tested on 28 3D CT images. Compared with manual segmentation, the mean Dice similarity coefficient can reach 86.11±8.02%. Experimental results show that our method can obtain more accurate segmentation results compared with existing segmentation methods.