Local Feature Matters: Cascade Multi-Scale MLP for Edge Segmentation of Medical Images
文献类型：期刊论文
| 作者 | Lv, Jinkai4; Hu, Yuyong4; Fu, Quanshui5; Hu, Yuqiang6; Lv, Lin7; Yang, Guoqing5; Li, Jinpeng1; Zhao, Yi2,3 |
| 刊名 | IEEE TRANSACTIONS ON NANOBIOSCIENCE
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 22期号:4页码:828-835 |
| ISSN号 | 1536-1241 |
| 关键词 | MLP medical image segmentation semantic segmentation |
| DOI | 10.1109/TNB.2023.3276473 |
| 英文摘要 | Convolution-based methods are increasingly being used in medical image segmentation tasks and have shown good performance, but there are always problems in segmenting edge parts. These methods all have the following challenges: 1) Previous methods do not highlight the relationship between foreground and background in segmented regions, which is helpful for complex segmentation edges, 2) inductive bias of the convolutional layer leads to the fact that the extracted information is mainly the main part of the segmented area, and cannot effectively perceive complex edge changes and the aggregation of small and many segmented areas,3) different regions around the segmentation edge have different reference values for segmentation, and the ordering of these values is more important when the segmentation task is more complex. To address these challenges, we propose the CM-MLP framework on Multi-scale Feature Interaction (MFI) block and Axial Context Relation Encoder (ACRE) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the Cascade Multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously, using Squeeze and Excitation in Space(SES) to process and redistribute the weights of all windows in Cascade MLP and utilize a cascade multi-scale mechanism to fuse discrete local information gradually. Then, multiple ACRE blocks cooperate with the deep supervision mechanism to gradually explore the boundary relationship between the foreground and the background, and gradually fine-tune the edges of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.98%, 96.67%, and 83.83% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. |
| 资助项目 | Research on Intelligent Multimodal Data Integration Algorithm and Its Application in Medical Treatment Foundation[E161080] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001082250700016 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21103]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Yang, Guoqing; Li, Jinpeng; Zhao, Yi |
| 作者单位 | 1.Univ Chinese Acad Sci, Ningbo Inst Life & Hlth Ind, Ningbo 315010, Peoples R China 2.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450046, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100190, Peoples R China 4.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450046, Peoples R China 5.Suining Cent Hosp, Suining 629000, Peoples R China 6.Nanchang Univ, Sch Math & Comp Sci, Nanchang 330047, Peoples R China 7.Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610056, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lv, Jinkai,Hu, Yuyong,Fu, Quanshui,et al. Local Feature Matters: Cascade Multi-Scale MLP for Edge Segmentation of Medical Images[J]. IEEE TRANSACTIONS ON NANOBIOSCIENCE,2023,22(4):828-835. |
| APA | Lv, Jinkai.,Hu, Yuyong.,Fu, Quanshui.,Hu, Yuqiang.,Lv, Lin.,...&Zhao, Yi.(2023).Local Feature Matters: Cascade Multi-Scale MLP for Edge Segmentation of Medical Images.IEEE TRANSACTIONS ON NANOBIOSCIENCE,22(4),828-835. |
| MLA | Lv, Jinkai,et al."Local Feature Matters: Cascade Multi-Scale MLP for Edge Segmentation of Medical Images".IEEE TRANSACTIONS ON NANOBIOSCIENCE 22.4(2023):828-835. |
入库方式： OAI收割
来源：计算技术研究所
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