A 2.5D semantic segmentation of the pancreas using attention guided dual context embedded U-Net

Automatic segmentation of the pancreas from medical images is important for clinical assessment of pancreas-related diseases. However, pancreatic segmentation based on computer tomography (CT) images is time-consuming and prone to errors because of the variances in shape and texture. Since various studies based on 2D/3D convolution neural networks (CNNs) have achieved encouraging performance for medical image segmentation, the 2D methods enjoy low inference time but suffer from a lack of 3D information. 3D methods are superior in performance for difficult targets requiring contextual information, but encounter the issue of high computational cost. Thus, we proposed a 2.5D segmentation method for pancreatic segmentation to balance utilizing contextual information and the high computational cost. This represents the 3D structural relationship among contiguous slices in a special representation. In the preprocessing stage, light-weight 3D voxels and the corresponding label mapping method were designed to explicitly express the differences in the target structure in contiguous slices. This would enable the network to learn spatial relationships directly. A 2D CNN embedded multi-attention mechanism and dual-context feature fusion method were designed to describe 3D information through 2D operations. In the post-processing stage, a fusion method was used to refine the segmentation results. The proposed method was evaluated on an abdominal contrast-enhanced CT dataset. Results showed Dice was 87.19%. Compared to the corresponding 2D and 3D methods, the proposed 2.5D method improved Dice by 1.14% and 2.80%, and was 60 times faster than the 3D method by using 0.1 times of the trainable parameters. Moreover, evaluations were performed on the NIH Pancreas-CT dataset, and the proposed 2.5D method achieved better segmentation performance than state-of-the-art methods.