Merging Context Clustering With Visual State Space Models for Medical Image Segmentation

Yun Zhu;Dong Zhang;Yi Lin;Yifei Feng;Jinhui Tang
{"title":"Merging Context Clustering With Visual State Space Models for Medical Image Segmentation","authors":"Yun Zhu;Dong Zhang;Yi Lin;Yifei Feng;Jinhui Tang","doi":"10.1109/TMI.2025.3525673","DOIUrl":null,"url":null,"abstract":"Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by excelling in long-range feature iterations with linear complexity. However, existing ViM approaches overlook the importance of preserving short-range local dependencies by directly flattening spatial tokens and are constrained by fixed scanning patterns that limit the capture of dynamic spatial context information. To address these challenges, we introduce a simple yet effective method named context clustering ViM (CCViM), which incorporates a context clustering module within the existing ViM models to segment image tokens into distinct windows for adaptable local clustering. Our method effectively combines long-range and short-range feature interactions, thereby enhancing spatial contextual representations for medical image segmentation tasks. Extensive experimental evaluations on diverse public datasets, i.e., Kumar, CPM17, ISIC17, ISIC18, and Synapse, demonstrate the superior performance of our method compared to current state-of-the-art methods. Our code can be found at <uri>https://github.com/zymissy/CCViM</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2131-2142"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical imaging","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10824894/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by excelling in long-range feature iterations with linear complexity. However, existing ViM approaches overlook the importance of preserving short-range local dependencies by directly flattening spatial tokens and are constrained by fixed scanning patterns that limit the capture of dynamic spatial context information. To address these challenges, we introduce a simple yet effective method named context clustering ViM (CCViM), which incorporates a context clustering module within the existing ViM models to segment image tokens into distinct windows for adaptable local clustering. Our method effectively combines long-range and short-range feature interactions, thereby enhancing spatial contextual representations for medical image segmentation tasks. Extensive experimental evaluations on diverse public datasets, i.e., Kumar, CPM17, ISIC17, ISIC18, and Synapse, demonstrate the superior performance of our method compared to current state-of-the-art methods. Our code can be found at https://github.com/zymissy/CCViM.
融合上下文聚类与视觉状态空间模型的医学图像分割
医学图像分割需要全局和局部特征表示的聚合,这对当前处理远程和短程特征交互的方法提出了挑战。最近,视觉曼巴(ViM)模型已经成为解决模型复杂性的有前途的解决方案,因为它擅长线性复杂性的远程特征迭代。然而,现有的ViM方法忽略了通过直接平坦化空间标记来保持短期局部依赖关系的重要性,并且受到固定扫描模式的限制,这些模式限制了动态空间上下文信息的捕获。为了解决这些挑战,我们引入了一种简单而有效的方法,称为上下文聚类ViM (CCViM),它在现有的ViM模型中集成了一个上下文聚类模块,将图像标记分割到不同的窗口中,以适应本地聚类。我们的方法有效地结合了远程和近距离特征交互,从而增强了医学图像分割任务的空间上下文表示。在不同的公共数据集(即Kumar, CPM17, ISIC17, ISIC18和Synapse)上进行了广泛的实验评估,证明了我们的方法与当前最先进的方法相比具有优越的性能。我们的代码可以在https://github.com/zymissy/CCViM上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信