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

Abstract

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 presented to enhance information gradient flow for the stability of the training process and easier convergence at the fine stage. A novel Corollary Atrous Spatial Pyramid Pooling Module (CASPP-Module) is proposed to simultaneously capture low-level details and high-level global context information to enhance the pancreas segmentation accuracy, extract global and local spatial data, and detect the pancreas at varied scales. On the publicly available National Institute of Health (NIH) pancreas dataset, our proposed EcD-Net surpasses previous state-of-the-art methods with a mean Dice Similarity Coefficient (DSC) of 88.84 %, mean Precision (PRE), Recall (REC), and Intersection over Union (IoU) of 91.85 %, 88.21 %, and 89.94 % respectively, and it also records the lowest standard deviations showing the robustness and steadiness of our proposed method.

Keywords

• Pancreas segmentation
• Corollary atrous spatial pooling
• Coarse-to-Fine
• Deep learning
• Dimension
• Coarse segmentation