IEEE transactions on medical imaging最新文献

{"title":"Echocardiography Video Segmentation via Mamba-Based Spatiotemporal Synergistic Network and Adaptive-Dynamic Learning","authors":"Yimu Sun;Guilian Chen;Jingxing Guo;Huisi Wu","doi":"10.1109/TMI.2026.3697520","DOIUrl":"10.1109/TMI.2026.3697520","url":null,"abstract":"Automatic echocardiography video segmentation is crucial for accurate diagnosis of cardiovascular diseases, as high-quality segmentation significantly improves automated lesion detection. However, deep learning methods still face challenges including speckle noise, dynamic ventricular changes, limited annotations, and the requirement for real-time inference in clinical practice. In this paper, we propose MSSNet, a novel semi-supervised method based on the efficient sequence modeling architecture Mamba, to address these challenges. To enhance noise robustness and foreground tracking, we design a flexible and efficient spatiotemporal synergistic guidance (SSG) module that leverages attention weights from historical frames to guide subsequent segmentation. By incorporating stable structural context and modeling inter-frame dependencies through weight propagation, SSG effectively mitigates segmentation errors caused by strong local noise and ventricular dynamics while maintaining low computational complexity. To alleviate limited annotation, we further introduce two semi-supervised modules: region-wise adaptive cross-mix (RAC) and dynamic offset correction (DOC). RAC simulates clinically plausible samples via regional mixing to enrich semantic details and strengthen feature learning, while DOC continuously integrates features from high-quality pseudo-labels during training. Experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that MSSNet outperforms existing SOTA methods in segmentation accuracy and achieves notable improvements in inference speed. The code is available at <uri>https://github.com/SSS666-klk/MSSNet</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4303-4314"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148045156","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}

{"title":"Visualizing Definitional Divergence in High-Dimensional Data by Manifold Alignment: Application to 3D Right Ventricular Strain Computations","authors":"Maxime Di Folco;Gabriel Bernardino;Patrick Clarysse;Nicolas Duchateau","doi":"10.1109/TMI.2026.3698240","DOIUrl":"10.1109/TMI.2026.3698240","url":null,"abstract":"Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence. Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4327-4338"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148059122","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}

{"title":"Morphology-Preserving Holotomography: Quantitative Analysis of 3-D Organoid Dynamics","authors":"ChulMin Oh;Jimin Cho;Juyeon Park;Hoyeon Lee;YongKeun Park","doi":"10.1109/TMI.2026.3698497","DOIUrl":"10.1109/TMI.2026.3698497","url":null,"abstract":"Organoids are three-dimensional (3D) in vitro models for studying tissue development, disease progression, and physiological responses. Holotomography (HT) enables long-term, label-free imaging of live organoids by reconstructing volumetric refractive-index (RI) maps, but quantitative analysis is limited by the missing-cone artifact, which introduces anisotropic resolution and axial distortion. Here, we present a quantitative analysis framework that addresses the missing-cone problem at the level of image representation rather than reconstruction. We introduce morphology-preserving holotomography (MP-HT), a torus-shaped spatial filtering strategy that emphasizes high-spatial-frequency RI texture while suppressing low-frequency components most susceptible to missing-cone-induced distortion. Based on MP-HT, we develop a 3D segmentation pipeline for robust separation of epithelial and luminal structures, together with a model-based RI quantification approach that incorporates the system point spread function to enable morphology-independent estimation of dry-mass density and total dry mass. We apply the framework to long-term imaging of live hepatic organoids undergoing expansion, collapse, and fusion. In representative organoids, the framework provides consistent segmentation across diverse geometries and enables quantitative characterization of epithelial-lumen remodeling, collapse-associated loss of morphometric stability, and transient biophysical fluctuations during fusion. Overall, this work establishes a physically transparent and reproducible approach for quantitative, label-free analysis of organoid dynamics in 3D.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4380-4394"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145664","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}

{"title":"Multi-View Chest X-Ray Vision-Language Pre-Training via Semantic-Aware Masked Language Modeling and High-Order Alignment","authors":"Lihong Qiao;Jingya Gong;Yucheng Shu;Lifang Zhou;Ximing Xu;Baobin Li;Weisheng Li;Baiying Lei","doi":"10.1109/TMI.2026.3700856","DOIUrl":"10.1109/TMI.2026.3700856","url":null,"abstract":"Chest X-Ray Vision-Language pretraining (VLP) leverages large-scale radiograph-report pairs to develop joint image-text representations, demonstrating significant potential for medical image diagnosis. However, existing VLP approaches often overlook the multi-view nature of chest X-Rays, and some multi-view methods apply uniform feature fusion, neglecting view-key semantic contributions. Moreover, random cross-modal Masked Language Modeling (MLM) fails to facilitate effective interactions, impeding representation alignment. Additionally, global alignment in VLP may lead to the false-negative problem. To address these limitations, we propose a novel medical VLP framework comprising three core components. First, a Key Semantics-enhanced Multi-view MLM module aggregates pathology-relevant patches across views, providing semantically rich supervision for MLM. A local semantics enhancing approach, which identifies and aggregates pathology-relevant key patches across views to guide MLM. Second, a Frontal-Lateral Alignment module extracts view-specific pathological features, ensuring semantic consistency and preserving critical information during aggregation. This module independently extracts pathological features from both views to preserve view-specific information while ensuring semantic consistency, which mitigates the loss of crucial information during aggregation. Third, a High-order Semantic Alignment approach mitigates false-negative issues by aligning features with semantically consistent clusters, enhancing global alignment through prototype-level semantics. Extensive experiments across seven public datasets demonstrate that our framework outperforms state-of-the-art methods in four downstream tasks, validating its efficacy. The code is available at <uri>https://github.com/sajiutea/F-L</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4406-4419"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177227","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}

{"title":"Medical Referring Image Segmentation via Next-Token Mask Prediction","authors":"Xinyu Chen;Yiran Wang;Gaoyang Pang;Jiafu Hao;Chentao Yue;Luping Zhou;Yonghui Li","doi":"10.1109/TMI.2026.3705770","DOIUrl":"10.1109/TMI.2026.3705770","url":null,"abstract":"Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent approaches usually involve complex design of multimodal fusion or multi-stage decoders. In this work, we propose NTP-MRISeg, a novel framework that reformulates MRIS as an autoregressive next-token prediction task over a unified multimodal sequence of tokenized image, text, and mask representations. This formulation streamlines model design by eliminating the need for modality-specific fusion and external segmentation models, and supports a unified architecture for end-to-end training. It also enables the use of pretrained tokenizers from emerging large-scale multimodal models, enhancing generalization and adaptability. More importantly, to address challenges under this formulation—such as exposure bias, long-tail token distributions, and fine-grained lesion edges—we propose three novel strategies: 1) a Next-k Token Prediction (NkTP) scheme to reduce cumulative prediction errors, 2) Token-level Contrastive Learning (TCL) to enhance boundary sensitivity and mitigate long-tail distribution effects, and 3) a memory-based Hard Error Token (HET) optimization strategy that emphasizes difficult tokens during training. Extensive experiments on the QaTa-COV19 and MosMedData+ datasets demonstrate that NTP-MRISeg achieves new state-of-the-art performance, offering a streamlined and effective alternative to traditional MRIS pipelines. The code is available at: <uri>https://github.com/c1oTTpD/MRIS</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4579-4590"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148299406","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}

{"title":"PRAD++: Toward Robust Periapical Radiograph Analysis Through Dataset and Model Advancements","authors":"Zhenhuan Zhou;Yuchen Zhang;Peng Wang;Xiaohang Guan;Tao Li","doi":"10.1109/TMI.2026.3698273","DOIUrl":"10.1109/TMI.2026.3698273","url":null,"abstract":"With the growing application of deep learning (DL) in dental image analysis, numerous datasets and models have been proposed. Periapical radiographs (PR), as one of the most common imaging modalities in clinical dentistry, play a critical role in endodontics. However, due to the high cost of manual annotation and interpretation challenges caused by poor projection and imaging artifacts, publicly available high-quality PR datasets remain scarce, severely limiting the development of DL-based PR analysis models that rely on large-scale annotated data. To address this issue, we introduce PRAD++, a large-scale PR analysis dataset annotated by clinical experts, consisting of 10,000 PR images with multi-level annotations, including 9 pixel-level segmentation categories and 17 image-level classification labels. Building upon PRAD++, we propose PRNet++, an end-to-end PR analysis network. The framework leverages the Multi-scale Wavelet Convolution (MWCN) network and the Channel Fusion Attention (CFA) mechanism to effectively model and integrate multi-scale features. In addition, an Expert Prior Injection (EPI) loss is designed to incorporate domain-specific dental knowledge into the learning process, refining classification predictions based on segmentation outputs to ensure accuracy and clinical interpretability. Extensive experiments on the PRAD++ dataset demonstrate that PRNet++ consistently outperforms state-of-the-art (SOTA) methods, achieving an average DSC of 81.25% for segmentation, alongside macro- and micro-averaged PR-AUCs of 66.58% and 79.10% for classification. Significantly surpassing the runner-up, PRNet++ exhibits enhanced robustness and interpretability in clinically challenging categories. Furthermore, comprehensive ablation and visualization analyses validate the efficacy of individual components and the parameter sensitivity of the EPI loss.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4339-4350"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148059048","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}

{"title":"MotionDPS: Motion-Compensated 3-D Brain MRI Reconstruction","authors":"Antonio Ortiz-Gonzalez;Erich Kobler;Lukas Schletter;Alexander Effland","doi":"10.1109/TMI.2026.3705847","DOIUrl":"10.1109/TMI.2026.3705847","url":null,"abstract":"Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective motion compensation remains challenging, particularly in accelerated acquisitions, due to the ill-posed nature of the joint reconstruction and motion estimation problem. In this work, we propose a unified Bayesian framework for motion-compensated 3-D MRI that jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. Our approach integrates pretrained 3-D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model. Inference is performed by alternating diffusion posterior image updates with efficient proximal optimization steps for motion and coil sensitivity estimation, enabling fully unsupervised reconstruction without the need for paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness compared to state-of-the-art classical and learning-based motion correction techniques, particularly in the presence of severe motion and high acceleration.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4591-4604"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11574503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148299405","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}

{"title":"Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiography","authors":"Lin Zhao;Xin Yu;Yikang Liu;Xiao Chen;Eric Z. Chen;Terrence Chen;Shanhui Sun","doi":"10.1109/TMI.2025.3623507","DOIUrl":"10.1109/TMI.2025.3623507","url":null,"abstract":"Accurate correspondence matching in coronary angiography images is crucial for reconstructing 3D coronary artery structures, which is essential for precise diagnosis and treatment planning of coronary artery disease (CAD). Traditional matching methods for natural images often fail to generalize to X-ray images due to inherent differences such as lack of texture, lower contrast, and overlapping structures, compounded by insufficient training data. To address these challenges, we propose a novel pipeline that generates realistic paired coronary angiography images using a diffusion model conditioned on 2D projections of 3D reconstructed meshes from Coronary Computed Tomography Angiography (CCTA), providing high-quality synthetic data for training. Additionally, we employ large-scale image foundation models to guide feature aggregation, enhancing correspondence matching accuracy by focusing on semantically relevant regions and keypoints. Our approach demonstrates superior matching performance on synthetic datasets and effectively generalizes to real-world datasets, offering a practical solution for this task. Furthermore, our work investigates the efficacy of different foundation models in correspondence matching, providing novel insights into leveraging advanced image foundation models for medical imaging applications.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4190-4200"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338362","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}

{"title":"HiAdapter: Histopathology-Induced Adapter for Pathology Foundation Models","authors":"Qingyang Liu;Peng Xie;Zhehao Dai;Xiangzhi Bai","doi":"10.1109/TMI.2026.3694387","DOIUrl":"10.1109/TMI.2026.3694387","url":null,"abstract":"With the rapid development of pathology foundation models, there is a growing demand for efficient fine-tuning strategies tailored to downstream tasks. However, existing parameter-efficient fine-tuning approaches are largely task-agnostic and exhibit limited generalization to histopathological images, particularly for unseen cancers and stains, due to substantial stain variability and the complexity of tissue microenvironments. To address these challenges, we present Histopathology-induced Adapter (HiAdapter), which incorporates domain-specific insights into staining and imaging mechanisms of histopathology. HiAdapter reconstructs stain-invariant representations via a Stain-invariant Adapter (S-Adapter) and integrates morphological features through a Morphology-aware Adapter (M-Adapter), effectively bridging the gap between low-level optical properties and high-level tissue semantics. Additionally, we introduce a Pathology Prototypical Contrastive Loss (PPCLoss) to reduce inter-class similarity and mitigate intra-class heterogeneity, enhancing feature discriminability. Extensive experiments using three pathology foundation models (CTransPath, CONCH and UNI) across six benchmarks, including two public datasets, an osteosarcoma tissue classification dataset (56,178 patches) and a chondrosarcoma necrosis classification dataset (3,867 patches) for unseen cancers generalization, as well as an IHC-stained dataset (4,967 patches) and an HIF1A IHC-stained dataset (4,433 patches) for unseen stains generalization, demonstrate the effectiveness of HiAdapter in both efficiency and accuracy. HiAdapter achieves an average improvement of 2.15 in F1 and 1.55 in accuracy over the second-best performer, maintaining strong biological and diagnostic interpretability. External validation on an independent osteosarcoma dataset (9,535 patches) and WSI-level survival analysis (178 slides) further confirm the superior generalizability and underscore the potential for patient-level diagnosis and prognosis in clinical practice. Our code is available at <uri>https://github.com/idata-ora/HiAdapter</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4212-4225"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977997","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}

{"title":"Leveraging Text-Modulated Semantic Guidance for Low-Light Endoscopic Image Enhancement","authors":"Hong Wang;Zhijian Wu;Haodu Fang;Dong Wei;Jinghan Sun;Yefeng Zheng;Jianhua Ma","doi":"10.1109/TMI.2026.3694909","DOIUrl":"10.1109/TMI.2026.3694909","url":null,"abstract":"Low light conditions in endoscopic imaging would lead to poor visibility, reduced contrast, and increased noise, which may hinder accurate diagnosis and surgical guidance. Against this low-light endoscopic image enhancement (LLEIE) task, inspired by the remarkable performance of pretrained CLIP in downstream vision tasks, in this paper, we carefully investigate the pretrained priors of CLIP and embed them into a text-modulated semantic-aware discriminator (TMSD). Through the adversarial learning mechanism, the discriminator can be easily integrated into different low-light enhancement baselines for helping them accomplish better visual restoration effects without incurring any extra inference cost. Specifically, to make the foundation model CLIP suitable for the LLEIE task, we initially propose a prompt learning procedure to obtain the text embedding and image semantics corresponding to the normal-light endoscopic imaging scenario. Building upon the acquired text prior and image semantic priors, we devise a text modulator to synergize these two priors, yielding a richer semantic representation. Leveraging the convolutional modulation and cross-attention mechanisms, we blend this semantic guidance information into the discriminator, thereby fostering the fine-grained distribution learning of normal-light endoscopic images in visual semantics and guiding different enhancement baselines achieving higher visual quality. Based on five public benchmark datasets, including three synthetic datasets, one real clinical dataset, and one clinical downstream segmentation dataset, we comprehensively evaluate the effectiveness of our proposed TMSD. Extensive experiments substantiate that the integration of the proposed TMSD enables seven representative baselines to obtain better perceptual quality, especially in the cross-domain clinical generalization scenario. Besides, the downstream segmentation accuracy can be evidently improved, showing the favorable application potential of the proposed TMSD. Moreover, to comprehensively evaluate the generality of our TMSD framework, we successfully apply it to a new and classic metal artifact reduction task. It is worth mentioning that our TMSD does not incur any extra computational cost during inference.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"45 8","pages":"4226-4239"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147978084","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}