Medical image analysis最新文献

{"title":"Anatomical prior-guided implicit segmentation of challenging thin and tubular anatomical structures in CBCT images","authors":"Chenfan Xu,Zhentao Liu,Haoshen Wang,Siyu Chen,Jiepeng Wang,Guangying Song,Zhiming Cui","doi":"10.1016/j.media.2026.104357","DOIUrl":"https://doi.org/10.1016/j.media.2026.104357","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"21 1","pages":"104357"},"PeriodicalIF":10.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PaSTA: Fast parametric inference of significance for spatial associations between brain maps","authors":"Yuanzhe Liu,Andrew Zalesky","doi":"10.1016/j.media.2026.104360","DOIUrl":"https://doi.org/10.1016/j.media.2026.104360","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"2 1","pages":"104360"},"PeriodicalIF":10.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical visual prompt disentanglement for continual rare disease diagnosis","authors":"Yang Hu,Yuanyuan Chen,Qingjie Zeng,Yong Xia","doi":"10.1016/j.media.2026.104355","DOIUrl":"https://doi.org/10.1016/j.media.2026.104355","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"21 1","pages":"104355"},"PeriodicalIF":10.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mind the Gap: Continuous magnification sampling for pathology foundation models","authors":"Alexander Möllers,Julius Hense,Florian Schulz,Timo Milbich,Maximilian Alber,Lukas Ruff","doi":"10.1016/j.media.2026.104327","DOIUrl":"https://doi.org/10.1016/j.media.2026.104327","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"8 1","pages":"104327"},"PeriodicalIF":10.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149088771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble-based uncertainty enables large-scale 3d segmentation quality control in population imaging","authors":"Tobias Norajitra,Tobias Nonnenmacher,Tobias Haueise,Fabian Isensee,Maximilian Zenk,Lea J. Michel,Jan Warszawski,Marc-Nicolas von Itter,Steffen Ringhof,Thoralf Niendorf,Jessica Krepel,Manuela Harries,Rafael Mikolajczyk,Jochen Hirsch,Daniel C. Hoinkiss,Thomas Kroencke,Josua Decker,Filippo Michelotti,Christoph M. Friedrich,Johannes Haubold,Tobias Pischon,Sigrid Thierry,Stefan N. Willich,Carsten O. Schmidt,Jakob Weiß,Susanne Rospleszcz,Hans-Ulrich Kauczor,Jürgen Machann,Fritz Schick,Christopher L. Schlett,Fabian Bamberg,Johanna Nattenmüller,Klaus Maier-Hein","doi":"10.1016/j.media.2026.104351","DOIUrl":"https://doi.org/10.1016/j.media.2026.104351","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"11 1","pages":"104351"},"PeriodicalIF":10.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomy-informed multi-task framework for visceral artery aneurysm screening on non-contrast CT.","authors":"Zhuowei Xu, Tianyi Liu, Long Bai, Hongqiu Wang, Jianfeng Zhang, Zirui Wang, Han Li, Yujian Hu, Min Zhou, Jianwen Ning, Minfeng Xu, Hongkun Zhang, Nassir Navab, S Kevin Zhou","doi":"10.1016/j.media.2026.104269","DOIUrl":"https://doi.org/10.1016/j.media.2026.104269","url":null,"abstract":"<p><p>Visceral artery aneurysm (VAA) screening on non-contrast CT (NCCT) is fundamentally a structure-constrained representation learning problem under weak vascular contrast. In NCCT, visceral arteries lack explicit lumen enhancement, and aneurysms often appear only as subtle local dilatations embedded in thin and branching vascular structures. This makes conventional intensity-driven segmentation unreliable and demands models that can preserve vascular continuity, capture subtle lesion morphology, and leverage anatomical context at the same time. To address this problem, we propose an anatomy-informed multi-task framework for NCCT-based VAA screening. Built on a Mamba-based segmentation backbone, our method reshapes token propagation according to vascular structure, enabling anatomy-consistent long-range dependency modeling without changing the original state space dynamics. To further strengthen the learned representation, we incorporate hierarchical anatomical supervision through global vessel-location classification and local patch-wise lesion prediction, so that the embedding is guided by both coarse anatomical semantics and fine-grained lesion evidence. We further introduce a prototype-based inference strategy that uses feature-space similarity to suppress morphologically confusing false positives. Extensive experiments on internal and external cohorts show that the proposed framework improves lesion segmentation, reduces false positives, and generalizes robustly across multiple cohorts. These results demonstrate that geometry-aware and anatomy-informed representation learning provides an effective and practical solution for VAA opportunistic screening on NCCT.</p>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":" ","pages":"104269"},"PeriodicalIF":14.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MICCAI STS 2024 challenge: Semi-supervised instance-level tooth segmentation in panoramic X-ray and CBCT images","authors":"Yaqi Wang ,&nbsp;Zhi Li ,&nbsp;Chengyu Wu ,&nbsp;Jun Liu ,&nbsp;Yifan Zhang ,&nbsp;Jiaxue Ni ,&nbsp;Qian Luo ,&nbsp;Jialuo Chen ,&nbsp;Hongyuan Zhang ,&nbsp;Jin Liu ,&nbsp;Can Han ,&nbsp;Kaiwen Fu ,&nbsp;Changkai Ji ,&nbsp;Xinxu Cai ,&nbsp;Jing Hao ,&nbsp;Zhihao Zheng ,&nbsp;Shi Xu ,&nbsp;Junqiang Chen ,&nbsp;Xiaoyang Yu ,&nbsp;Qianni Zhang ,&nbsp;Huiyu Zhou","doi":"10.1016/j.media.2026.103986","DOIUrl":"10.1016/j.media.2026.103986","url":null,"abstract":"<div><div>Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We organized the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge at MICCAI 2024. We provided a large-scale dataset comprising over 90,000 2D images and 3D axial slices, which includes 2380 OPG images and 330 CBCT scans, all featuring detailed instance-level FDI annotations on part of the data. The challenge attracted 114 (OPG) and 106 (CBCT) registered teams. To ensure algorithmic excellence and full transparency, we rigorously evaluated the valid, open-source submissions from the top 10 (OPG) and top 5 (CBCT) teams, respectively. All successful submissions were deep learning-based SSL methods. The winning semi-supervised models demonstrated impressive performance gains over a fully-supervised nnU-Net baseline trained only on the labeled data. For the 2D OPG track, the top method improved the Instance Affinity (IA) score by over 44 percentage points. For the 3D CBCT track, the winning approach boosted the Instance Dice score by 61 percentage points. This challenge demonstrates the potential benefit benefit of SSL for complex, instance-level medical image segmentation tasks where labeled data is scarce. The most effective approaches consistently leveraged hybrid semi-supervised frameworks that combined knowledge from foundational models like SAM with multi-stage, coarse-to-fine refinement pipelines. Both the challenge dataset and the participants’ submitted code have been made publicly available on GitHub (<span><span>https://github.com/ricoleehduu/STS-Challenge-2024</span><svg><path></path></svg></span>), ensuring transparency and reproducibility.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103986"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurobridge: Bridging functional and structural brain networks via neural coupling and consistency-Guided dynamic graph learning","authors":"Yueying Li ,&nbsp;Rui Dong ,&nbsp;Xiaoyun Liu ,&nbsp;Yonggui Yuan ,&nbsp;Youyong Kong","doi":"10.1016/j.media.2026.103993","DOIUrl":"10.1016/j.media.2026.103993","url":null,"abstract":"<div><div>Modern medical imaging provides important insights into brain network analysis. Functional brain networks are used to characterize the functional connectivity patterns in resting or task states, and structural brain networks reflect the integrity and connectivity strength of macro-scale pathways. However, there are differences in data structure, information representation and spatial resolution between the both, and how to effectively fuse the information from these two modalities to mine potential cross-modal representations has become a key challenge in current research. In this paper, we propose the <strong>NeuroBridge</strong>, which enable the interaction between different modalities and the extraction of discriminative joint representations through coupling at the macro-scale level for brain network analysis. Specifically, the <em>Neural Synergy Coupling Module (NeuSCM)</em> performs structural-functional coupling in terms of brain region receptive fields. In order to enhance the inter-modal high-level semantic spatial coherence, we propose the <em>Consistency Anchor Guidance Module (CAGM)</em> for semantic calibration and convergence control of the fused representation space. Finally the <em>Dynamic Association Parsing Module (DAP)</em> captures the complex relationships between nodes and is used for final prediction and biomarker extraction. We conduct extensive experiments on disease prediction and gender classification tasks, and our results show that the prediction accuracy of our method outperforms that of SOTA single and multimodal methods, while our study provides new insights into multimodal brain network analysis.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103993"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformation-Recovery diffusion model (DRDM): Instance deformation for image manipulation and synthesis","authors":"Jian-Qing Zheng ,&nbsp;Yuanhan Mo ,&nbsp;Yang Sun ,&nbsp;Jiahua Li ,&nbsp;Fuping Wu ,&nbsp;Ziyang Wang ,&nbsp;Tonia Vincent ,&nbsp;Bartłomiej W Papież","doi":"10.1016/j.media.2026.103987","DOIUrl":"10.1016/j.media.2026.103987","url":null,"abstract":"<div><div>In medical imaging, diffusion models have shown great potential for synthetic image generation. However, these approaches often lack interpretable correspondence between generated and real images and can create anatomically implausible structures or illusions. To address these limitations, we propose the Deformation-Recovery Diffusion Model (DRDM), a novel diffusion-based generative model that emphasizes morphological transformation through deformation fields rather than direct image synthesis. DRDM introduces a topology-preserving deformation field generation strategy, which randomly samples and integrates multi-scale Deformation Velocity Fields (DVFs). DRDM is trained to learn to recover unrealistic deformation components, thus restoring randomly deformed images to a realistic distribution. This formulation enables the generation of diverse yet anatomically plausible deformations that preserve structural integrity, thereby improving data augmentation and synthesis for downstream tasks such as few-shot learning and image registration. Experiments on cardiac Magnetic Resonance Imaging and pulmonary Computed Tomography show that DRDM is capable of creating diverse, large-scale deformations, while maintaining anatomical plausibility of deformation fields. Additional evaluations on 2D image segmentation and 3D image registration tasks indicate notable performance gains, underscoring DRDM’s potential to enhance both image manipulation and generative modeling in medical imaging applications.</div><div>The project page: <span><span>https://jianqingzheng.github.io/def_diff_rec/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103987"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual selective gleason pattern-aware multiple instance learning with uncertainty regularization for grade group prediction in histopathology images","authors":"Xinyu Hao ,&nbsp;Hongming Xu ,&nbsp;Jingdong Zhang ,&nbsp;Qi Xu ,&nbsp;Ilkka Pölönen ,&nbsp;Fengyu Cong","doi":"10.1016/j.media.2026.104005","DOIUrl":"10.1016/j.media.2026.104005","url":null,"abstract":"<div><div>Accurate prediction of Gleason Grade Group (GG) is of great importance for prostate cancer risk stratification and treatment planning. Although multiple instance learning (MIL) methods have advanced Gleason grading, most existing studies overlook the domain knowledge that GG is determined by the joint contribution of different Gleason Patterns, thereby limiting both accuracy and interpretability. In this study, we propose DSPA-U-MIL, an uncertainty-driven dual-selective Gleason Pattern-aware MIL model for patient-level GG prediction. Our method learns representative features by integrating learnable pattern aggregation tokens with expert concept-guided patch-level aggregation, and incorporates a teacher-student knowledge distillation framework to simulate cooperative prediction among different Gleason Patterns. In addition, we introduce an uncertainty constraint to mitigate the impact of noisy labels and enhance prediction robustness. Extensive experiments on five datasets, comprising 10,809 whole slide images (WSIs) and 1133 tissue microarray (TMA) images, demonstrate that DSPA-U-MIL consistently outperforms existing MIL approaches in Gleason GG prediction. Among these datasets, our method achieves up to a 6.7% improvement in quadratic weighted kappa (QWK) score over the strong baseline CLAM, with the largest gain observed on TCGA-PRAD. Furthermore, the gating weight distributions of the student model are well aligned with pathologists’ Gleason Pattern annotations, reinforcing the interpretability of our approach. Our source code is available at <span><span>https://github.com/AlexNmSED/DSPA-MIL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 104005"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}