Parallel Multi‐Scale Network with Attention Mechanism for Pancreas Segmentation. Issue 1 (28th October 2021)

• Record Type: Journal Article
• Authors: Long, Jianwu; Song, Xinlei; An, Yong; Li, Tong; Zhu, Jiangzhou
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 17:Issue 1(2022)
• Page Start: 110
• Page End: 119
• Publication Date: 2021-10-28
• Subjects: semantic segmentation -- attention module -- pancreas segmentation -- parallel multi‐scale network; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23493
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : In this paper, we address the task of segmenting small organs (i.e., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by complex and variable backgrounds. We propose a method that uses a parallel multi‐scale network with an attention mechanism for pancreas segmentation, which can better grasp the balance between the semantic segmentation, classification, and localization tasks. We use a parallel network to connect the feature maps between different bottleneck layers, which contain rich semantic information and complete spatial information. We apply an attention module to enhance the key features of semantic information. Then, we fuse the two modules and apply the fused module as attention information on the feature map to ensure the full fusion between contextual semantic information and spatial information, thereby improving segmentation accuracy. We conduct extensive experiments on the NIH pancreas segmentation data set. In particular, our model achieves a mean coefficient Dice of 86.6. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.