EcD-Net: Encoder-Corollary Atrous Spatial Pyramid Pooling-decoder network for automated pancreas segmentation of 2D CT images

EcD-Net: Encoder-Corollary Atrous Spatial Pyramid Pooling-decoder network for automated pancreas segmentation of 2D CT images

Isaac Baffour Senkyire, Kashala Kabe Gedeon, Emmanuel Freeman, Benjamin Ghansah, Zhe Liu

Automatic pancreas segmentation of CT scans enables physicians to identify and monitor the abnormalities in the pancreas. This facilitates intraoperative assistance, surgical planning, prognosis, and diagnosis. Nonetheless, the size and location of the pancreas in the CT image input data present a significant challenge for automatic segmentation, and the intricacy of the background region confounds deep segmentation networks. To solve this difficulty, we propose the Encoder-Corollary Atrous Spatial Pyramid Pooling-Decoder Network (EcD-Net) for locating and segmenting the pancreas. This two-tiered method begins with a coarse segmentation stage for locating the pancreas within the overall CT image. Using the detected image from the first tier, a fine segmentation network based on U-Net is applied to segment the target organ (pancreas). A novel Saturated Multi-view Dense Module (SMD- Module) is …

Elsevier

2024

https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rQvaaoMAAAAJ&cstart=20&pagesize=80&citation_for_view=rQvaaoMAAAAJ:4DMP91E08xMC

English