Pancreatic Cystic Neoplasms Segmentation Network Combining Dual Decoding and Global Attention Upsampling Modules

Abstract

The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.

Keywords

• medical image segmentation
• dganet
• deep learning
• pancreas