文献类型：期刊文献

英文题名：DSnet: a new dual-branch network for hippocampus subfield segmentation

作者：Zhu, Hancan[1,2];Cheng, Wangang[1];Hu, Keli[2];He, Guanghua[1]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, 900 ChengNan Rd, Shaoxing 312000, Zhejiang, Peoples R China;[2]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 312000, Zhejiang, Peoples R China

年份：2024

卷号：14

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：SCI-EXPANDED(收录号：WOS:001262863000020)、、Scopus(收录号：2-s2.0-85197670131)、WOS

基金：This work was supported by Humanities and Social Science Fund of Ministry of Education of China (23YJAZH232), Scientific Research Project of Shaoxing University (20210038) and Zhejiang Provincial Natural Science Foundation of China (LZ24F020006)

语种：英文

外文关键词：Hippocampal subfield segmentation; Deep learning; Dual-branch network; U-Net

外文摘要：The hippocampus is a critical component of the brain and is associated with many neurological disorders. It can be further subdivided into several subfields, and accurate segmentation of these subfields is of great significance for diagnosis and research. However, the structures of hippocampal subfields are irregular and have complex boundaries, and their voxel values are close to surrounding brain tissues, making the segmentation task highly challenging. Currently, many automatic segmentation tools exist for hippocampal subfield segmentation, but they suffer from high time costs and low segmentation accuracy. In this paper, we propose a new dual-branch segmentation network structure (DSnet) based on deep learning for hippocampal subfield segmentation. While traditional convolutional neural network-based methods are effective in capturing hierarchical structures, they struggle to establish long-term dependencies. The DSnet integrates the Transformer architecture and a hybrid attention mechanism, enhancing the network's global perceptual capabilities. Moreover, the dual-branch structure of DSnet leverages the segmentation results of the hippocampal region to facilitate the segmentation of its subfields. We validate the efficacy of our algorithm on the public Kulaga-Yoskovitz dataset. Experimental results indicate that our method is more effective in segmenting hippocampal subfields than conventional single-branch network structures. Compared to the classic 3D U-Net, our proposed DSnet improves the average Dice accuracy of hippocampal subfield segmentation by 0.57%.