IEEE transactions on medical imaging最新文献_第9页

Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow 基于自适应时空张量语义感知和记忆流的半监督超声心动图视频分割

IEEE transactions on medical imaging Pub Date : 2025-01-08 DOI: 10.1109/TMI.2025.3526955

Xiaodi Li;Chen Cui;Siyuan Shi;Hongwen Fei;Yue Hu

{"title":"Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow","authors":"Xiaodi Li;Chen Cui;Siyuan Shi;Hongwen Fei;Yue Hu","doi":"10.1109/TMI.2025.3526955","DOIUrl":"10.1109/TMI.2025.3526955","url":null,"abstract":"Accurate segmentation of cardiac structures in echocardiography videos is vital for diagnosing heart disease. However, challenges such as speckle noise, low spatial resolution, and incomplete video annotations hinder the accuracy and efficiency of segmentation tasks. Existing video-based segmentation methods mainly utilize optical flow estimation and cross-frame attention to establish pixel-level correlations between frames, which are usually sensitive to noise and have high computational costs. In this paper, we present an innovative echocardiography video segmentation framework that exploits the inherent spatio-temporal correlation of echocardiography video feature tensors. Specifically, we perform adaptive tensor singular value decomposition (t-SVD) on the video semantic feature tensor within a learnable 3D transform domain. By utilizing learnable thresholds, we preserve the principal singular values to reduce redundancy in the high-dimensional spatio-temporal feature tensor and enforce its potential low-rank property. Through this process, we can capture the temporal evolution of the target tissue by effectively utilizing information from limited labeled frames, thus overcoming the constraints of sparse annotations. Furthermore, we introduce a memory flow method that propagates relevant information between adjacent frames based on the multi-scale affinities to precisely resolve frame-to-frame variations of dynamic tissues, thereby improving the accuracy and continuity of segmentation results. Extensive experiments conducted on both public and private datasets validate the superiority of our proposed method over state-of-the-art methods, demonstrating improved performance in echocardiography video segmentation.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2182-2193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CTUSurv: A Cell-Aware Transformer-Based Network With Uncertainty for Survival Prediction Using Whole Slide Images ct篡位：一种基于细胞感知变压器的不确定性网络，用于使用整个幻灯片图像进行生存预测

IEEE transactions on medical imaging Pub Date : 2025-01-08 DOI: 10.1109/TMI.2025.3526848

Zhihao Tang;Lin Yang;Zongyi Chen;Li Liu;Chaozhuo Li;Ruanqi Chen;Xi Zhang;Qingfeng Zheng

{"title":"CTUSurv: A Cell-Aware Transformer-Based Network With Uncertainty for Survival Prediction Using Whole Slide Images","authors":"Zhihao Tang;Lin Yang;Zongyi Chen;Li Liu;Chaozhuo Li;Ruanqi Chen;Xi Zhang;Qingfeng Zheng","doi":"10.1109/TMI.2025.3526848","DOIUrl":"10.1109/TMI.2025.3526848","url":null,"abstract":"Image-based survival prediction through deep learning techniques represents a burgeoning frontier aimed at augmenting the diagnostic capabilities of pathologists. However, directly applying existing deep learning models to survival prediction may not be a panacea due to the inherent complexity and sophistication of whole slide images (WSIs). The intricate nature of high-resolution WSIs, characterized by sophisticated patterns and inherent noise, presents significant challenges in terms of effectiveness and trustworthiness. In this paper, we propose CTUSurv, a novel survival prediction model designed to simultaneously capture cell-to-cell and cell-to-microenvironment interactions, complemented by a region-based uncertainty estimation framework to assess the reliability of survival predictions. Our approach incorporates an innovative region sampling strategy to extract task-relevant, informative regions from high-resolution WSIs. To address the challenges posed by sophisticated biological patterns, a cell-aware encoding module is integrated to model the interactions among biological entities. Furthermore, CTUSurv includes a novel aleatoric uncertainty estimation module to provide fine-grained uncertainty scores at the region level. Extensive evaluations across four datasets demonstrate the superiority of our proposed approach in terms of both predictive accuracy and reliability.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 4","pages":"1750-1764"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Histo-Genomic Knowledge Association for Cancer Prognosis From Histopathology Whole Slide Images 从组织病理学整张幻灯片图像来看，组织基因组知识与癌症预后的关联

IEEE transactions on medical imaging Pub Date : 2025-01-07 DOI: 10.1109/TMI.2025.3526816

Zhikang Wang;Yumeng Zhang;Yingxue Xu;Seiya Imoto;Hao Chen;Jiangning Song

{"title":"Histo-Genomic Knowledge Association for Cancer Prognosis From Histopathology Whole Slide Images","authors":"Zhikang Wang;Yumeng Zhang;Yingxue Xu;Seiya Imoto;Hao Chen;Jiangning Song","doi":"10.1109/TMI.2025.3526816","DOIUrl":"10.1109/TMI.2025.3526816","url":null,"abstract":"Histo-genomic multi-modal methods have emerged as a powerful paradigm, demonstrating significant potential for cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this, we propose a novel Genome-informed Hyper-Attention Network, termed G-HANet, which is capable of effectively learning the histo-genomic associations during training to elevate uni-modal whole slide image (WSI)-based inference for the first time. Compared with the potential knowledge distillation strategy for this setting (i.e., distilling a multi-modal network to a uni-modal network), our end-to-end model is superior in training efficiency and learning cross-modal interactions. Specifically, the network comprises cross-modal associating branch (CAB) and hyper-attention survival branch (HSB). Through the genomic data reconstruction from WSIs, CAB effectively distills the associations between functional genotypes and morphological phenotypes and offers insights into the gene expression profiles in the feature space. Subsequently, HSB leverages the distilled histo-genomic associations as well as the generated morphology-based weights to achieve the hyper-attention modeling of the patients from both histopathology and genomic perspectives to improve cancer prognosis. Extensive experiments are conducted on five TCGA benchmarking datasets and the results demonstrate that G-HANet significantly outperforms the state-of-the-art WSI-based methods and achieves competitive performance with genome-based and multi-modal methods. G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology. The code is available at <uri>https://github.com/ZacharyWang-007/G-HANet</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2170-2181"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation? 添加一个模态真的对不完全多模态脑肿瘤分割有积极影响吗？

IEEE transactions on medical imaging Pub Date : 2025-01-07 DOI: 10.1109/TMI.2025.3526818

Yansheng Qiu;Kui Jiang;Hongdou Yao;Zheng Wang;Shin’ichi Satoh

{"title":"Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?","authors":"Yansheng Qiu;Kui Jiang;Hongdou Yao;Zheng Wang;Shin’ichi Satoh","doi":"10.1109/TMI.2025.3526818","DOIUrl":"10.1109/TMI.2025.3526818","url":null,"abstract":"Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2194-2205"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks 使用在线学习神经网络的3D电影MRI估计左心房位移和应变的高分辨率地图

IEEE transactions on medical imaging Pub Date : 2025-01-06 DOI: 10.1109/TMI.2025.3526364

Christoforos Galazis;Samuel Shepperd;Emma J. P. Brouwer;Sandro Queirós;Ebraham Alskaf;Mustafa Anjari;Amedeo Chiribiri;Jack Lee;Anil A. Bharath;Marta Varela

{"title":"High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks","authors":"Christoforos Galazis;Samuel Shepperd;Emma J. P. Brouwer;Sandro Queirós;Ebraham Alskaf;Mustafa Anjari;Amedeo Chiribiri;Jack Lee;Anil A. Bharath;Marta Varela","doi":"10.1109/TMI.2025.3526364","DOIUrl":"10.1109/TMI.2025.3526364","url":null,"abstract":"The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lacking appropriate acquisition and analysis tools. Here, we propose tools for the Analysis of Left Atrial Displacements and DeformatIons using online learning neural Networks (Aladdin) and present a technical feasibility study on how Aladdin can characterize 3D LA function globally and regionally. Aladdin includes an online segmentation and image registration network, and a strain calculation pipeline tailored to the LA. We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD), of which 2 had large left ventricular ejection fraction (LVEF) impairment. We additionally create an atlas of these biomarkers using the data from the healthy volunteers. Results showed that Aladdin can accurately track the LA wall across the cardiac cycle and characterize its motion and deformation. Global LA function markers assessed with Aladdin agree well with estimates from 2D Cine MRI. A more marked active contraction phase was observed in the healthy cohort, while the CVD <inline-formula> <tex-math>$text {LVEF}_{downarrow } $ </tex-math></inline-formula> group showed overall reduced LA function. Aladdin is uniquely able to identify LA regions with abnormal deformation metrics that may indicate focal pathology. We expect Aladdin to have important clinical applications as it can non-invasively characterize atrial pathophysiology. All source code and data are available at: <uri>https://github.com/cgalaz01/aladdin_cmr_la</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2056-2067"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction 医学图像重构中可控制条件扩散的分布外自适应

IEEE transactions on medical imaging Pub Date : 2025-01-06 DOI: 10.1109/TMI.2024.3524797

Riccardo Barbano;Alexander Denker;Hyungjin Chung;Tae Hoon Roh;Simon Arridge;Peter Maass;Bangti Jin;Jong Chul Ye

引用次数: 0

Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis 基于知识蒸馏和图像合成的通信高效联邦学习多器官分割

IEEE transactions on medical imaging Pub Date : 2025-01-06 DOI: 10.1109/TMI.2025.3525581

Soopil Kim;Heejung Park;Philip Chikontwe;Myeongkyun Kang;Kyong Hwan Jin;Ehsan Adeli;Kilian M. Pohl;Sang Hyun Park

{"title":"Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis","authors":"Soopil Kim;Heejung Park;Philip Chikontwe;Myeongkyun Kang;Kyong Hwan Jin;Ehsan Adeli;Kilian M. Pohl;Sang Hyun Park","doi":"10.1109/TMI.2025.3525581","DOIUrl":"10.1109/TMI.2025.3525581","url":null,"abstract":"Federated learning (FL) methods for multi-organ segmentation in CT scans are gaining popularity, but generally require numerous rounds of parameter exchange between a central server and clients. This repetitive sharing of parameters between server and clients may not be practical due to the varying network infrastructures of clients and the large transmission of data. Further increasing repetitive sharing results from data heterogeneity among clients, i.e., clients may differ with respect to the type of data they share. For example, they might provide label maps of different organs (i.e. partial labels) as segmentations of all organs shown in the CT are not part of their clinical protocol. To this end, we propose an efficient communication approach for FL with partial labels. Specifically, parameters of local models are transmitted once to a central server and the global model is trained via knowledge distillation (KD) of the local models. While one can make use of unlabeled public data as inputs for KD, the model accuracy is often limited due to distribution shifts between local and public datasets. Herein, we propose to generate synthetic images from clients’ models as additional inputs to mitigate data shifts between public and local data. In addition, our proposed method offers flexibility for additional finetuning through several rounds of communication using existing FL algorithms, leading to enhanced performance. Extensive evaluation on public datasets in few communication FL scenario reveals that our approach substantially improves over state-of-the-art methods.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2079-2092"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction 无监督动态MRI重构的时空内隐神经表征

IEEE transactions on medical imaging Pub Date : 2025-01-06 DOI: 10.1109/TMI.2025.3526452

Jie Feng;Ruimin Feng;Qing Wu;Xin Shen;Lixuan Chen;Xin Li;Li Feng;Jingjia Chen;Zhiyong Zhang;Chunlei Liu;Yuyao Zhang;Hongjiang Wei

{"title":"Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction","authors":"Jie Feng;Ruimin Feng;Qing Wu;Xin Shen;Lixuan Chen;Xin Li;Li Feng;Jingjia Chen;Zhiyong Zhang;Chunlei Liu;Yuyao Zhang;Hongjiang Wei","doi":"10.1109/TMI.2025.3526452","DOIUrl":"10.1109/TMI.2025.3526452","url":null,"abstract":"Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has emerged as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled <inline-formula> <tex-math>$boldsymbol {k}$ </tex-math></inline-formula>-space data, which only takes spatiotemporal coordinates as inputs and does not require any training on external datasets or transfer-learning from prior images. Specifically, the proposed method encodes the dynamic MRI images into neural networks as an implicit function, and the weights of the network are learned from sparsely-acquired (<inline-formula> <tex-math>$boldsymbol {k}$ </tex-math></inline-formula>, t)-space data itself only. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared state-of-the-art methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 0.6–2.0 dB in PSNR for high accelerations (up to <inline-formula> <tex-math>$40.8times $ </tex-math></inline-formula>). The high-quality and inner continuity of the images provided by INR exhibit great potential to further improve the spatiotemporal resolution of dynamic MRI. The code is available at: <uri>https://github.com/AMRI-Lab/INR_for_DynamicMRI</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2143-2156"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transcranial Photoacoustic Tomography De-Aberrated Using Boundary Elements 经颅光声断层成像使用边界元素去像差

IEEE transactions on medical imaging Pub Date : 2025-01-06 DOI: 10.1109/TMI.2025.3526000

Karteekeya Sastry;Yousuf Aborahama;Yilin Luo;Yang Zhang;Manxiu Cui;Rui Cao;Geng Ku;Lihong V. Wang

{"title":"Transcranial Photoacoustic Tomography De-Aberrated Using Boundary Elements","authors":"Karteekeya Sastry;Yousuf Aborahama;Yilin Luo;Yang Zhang;Manxiu Cui;Rui Cao;Geng Ku;Lihong V. Wang","doi":"10.1109/TMI.2025.3526000","DOIUrl":"10.1109/TMI.2025.3526000","url":null,"abstract":"Photoacoustic tomography holds tremendous potential for neuroimaging due to its functional magnetic resonance imaging (fMRI)-like functional contrast and greater specificity, richer contrast, portability, open platform, faster imaging, magnet-free and quieter operation, and lower cost. However, accounting for the skull-induced acoustic distortion remains a long-standing challenge due to the problem size. This is aggravated in functional imaging, where high accuracy is needed to detect minuscule functional changes. Here, we develop an acoustic solver based on the boundary-element method (BEM) to model the skull and de-aberrate the images. BEM uses boundary meshes and compression for superior computational efficiency compared to volumetric discretization-based methods. We demonstrate BEM’s higher accuracy and favorable scalability relative to the widely used pseudo-spectral time-domain method (PSTD). In imaging through an ex-vivo adult human skull, BEM outperforms PSTD in several metrics. Our work establishes BEM as a valuable and naturally suited technique in photoacoustic tomography and lays the foundation for BEM-based de-aberration methods.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 5","pages":"2068-2078"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Merging Context Clustering With Visual State Space Models for Medical Image Segmentation 融合上下文聚类与视觉状态空间模型的医学图像分割

IEEE transactions on medical imaging Pub Date : 2025-01-03 DOI: 10.1109/TMI.2025.3525673

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":"10.1109/TMI.2025.3525673","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":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0