PSDD-Net: A Dual-Domain Framework for Pancreatic Cancer Image Segmentation with Multi-scale Local-Dense Net

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers in the word. However, the diverse microenvironment, unclear boundaries, integrity destruction inter the slices, and enormous individual differences of tumors pose tremendous challenges to the segmentation process. To address these challenges, we proposed a physical-spiral dual-domain network (PSDD-Net) that combines the advantages of the spiral domain and the physical domain. First of all, the physical domain promotes integral representations of the tumor features, and the spiral domain protrudes the tumor region under CT multi-directions. As a result, the dual-domain framework makes the dual-domain feature simultaneously sent to the network to promote greater attention to the pancreatic region and reduce the interference of redundant background information. Secondly, we also present a multi-scale local-dense net (MSLD-Net) in the physical domain which contains local-channel dense block (LCDB) and multi-scale semantic feature extraction (MSSFE) module. The MSLD-Net grasps more multi-scale geometric information of the tumors and facilitates feature map fusion. Thirdly, a cross-domain aggregation (CDA) module is designed to interact bridging the two domains to interleave and integrate dual-domain complementary visual information. The extensive experiments on the clinical dataset show that our method obtained the DSC of 76.00 \(\%\) in abdominal CT, which outperformed the other state-of-the-art on pancreatic cancer segmentation results and demonstrated strong potential for clinical applications.