Medical image analysis最新文献

{"title":"Aligning personalized biomarker trajectories onto a common time axis: a connectome-based ODE model for Tau–Amyloid beta dynamics","authors":"Zheyu Wen ,&nbsp;George Biros ,&nbsp;the Alzheimer’s Disease Neuroimaging Initiative (ADNI)","doi":"10.1016/j.media.2025.103757","DOIUrl":"10.1016/j.media.2025.103757","url":null,"abstract":"<div><div>Abnormal tau and amyloid beta are two primary imaging biomarkers used to assist in the diagnosis of Alzheimer’s disease (AD). Recent efforts have focused on developing mechanism-based biophysical models to explain the spatiotemporal dynamics of these biomarkers. In this study, we adopt a connectome-based ODE model to capture the dynamics of tau and amyloid beta (<span><math><mrow><mtext>A</mtext><mi>β</mi></mrow></math></span>), aiming to predict personalized future values of these biomarkers. The ODE model includes diffusion, reaction, and clearance terms, and accounts for tau–<span><math><mrow><mtext>A</mtext><mi>β</mi></mrow></math></span> interactions. Additionally, it assumes a sparse initial condition (IC) of abnormalities, based on the assumption of localized initiation. Besides tau and <span><math><mrow><mtext>A</mtext><mi>β</mi></mrow></math></span>, brain atrophy is used as a marker of neurodegeneration. We discuss the mathematical model of atrophy integrated into the tau–<span><math><mrow><mtext>A</mtext><mi>β</mi></mrow></math></span> model. A common limitation in popular models is the use of chronological age as the time axis, which prevents the unification of subject trajectories onto a common time scale and hinders comprehensive cohort analysis. To address this issue, we use a normalized disease age that relates chronological age to biomarker values. In the ODE model, we use the disease age to track time and the biomarker dynamics. Furthermore, our analysis of region-of-interest-wise tau–<span><math><mrow><mtext>A</mtext><mi>β</mi></mrow></math></span> temporal correlation reveals that different regions of interest (ROIs) play distinct roles across various disease stages.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103757"},"PeriodicalIF":11.8,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893532","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":"Pixel-responsive optimization beamforming method for ultrasound transcranial imaging","authors":"Junyi Wang ,&nbsp;Tianhua Zhou ,&nbsp;Gaobo Zhang ,&nbsp;Boyi Li ,&nbsp;Xin Liu ,&nbsp;Dean Ta","doi":"10.1016/j.media.2025.103762","DOIUrl":"10.1016/j.media.2025.103762","url":null,"abstract":"<div><div>The propagation of acoustic waves through bone remains a longstanding challenge in transcranial ultrasound imaging. As a highly scattering medium, the skull causes significant distortions in the ultrasonic wavefield, introducing complex aberrations that hinder precise image reconstruction. Conventional delay-and-sum (DAS) algorithms, which process pixels independently, fail to account for inter-pixel relationships, limiting their ability to correct such distortions. To address this issue, we propose a Pixel-Responsive Optimization (PRO) Beamforming Method that leverages backscattered signals from compound plane waves. By constructing a pixel-response matrix and simulating a virtual acoustic lens, PRO isolates and aligns distorted fields with reference phases to restore near-ideal propagation. Experiments on bovine femur plates and a human skull demonstrate improved image resolution, recovery of submerged signals, and artifact suppression. PRO achieves up to a 90% improvement in full-width at half-maximum (FWHM) compared to DAS, requiring no prior assumptions and showing strong generalizability in complex scenarios through bone. This advancement holds promise for future <em>in vivo</em> transcranial brain imaging applications.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103762"},"PeriodicalIF":11.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860858","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":"Speckle2Self: Self-supervised ultrasound speckle reduction without clean data","authors":"Xuesong Li ,&nbsp;Nassir Navab ,&nbsp;Zhongliang Jiang","doi":"10.1016/j.media.2025.103755","DOIUrl":"10.1016/j.media.2025.103755","url":null,"abstract":"<div><div>Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. This dependency means that obtaining two independent noisy observations of the same scene, as required by pioneering Noise2Noise, is not feasible. Additionally, blind-spot networks also cannot handle US speckle noise due to its high spatial dependency. To address this challenge, we introduce Speckle2Self, a novel self-supervised algorithm for speckle reduction using only single noisy observations. The key insight is that applying a multi-scale perturbation (MSP) operation introduces tissue-dependent variations in the speckle pattern across different scales, while preserving the shared anatomical structure. This enables effective speckle suppression by modeling the clean image as a low-rank signal and isolating the sparse noise component. To demonstrate its effectiveness, Speckle2Self is comprehensively compared with conventional filter-based denoising algorithms and SOTA learning-based methods, using both realistic simulated US images and human carotid US images. Additionally, data from multiple US machines are employed to evaluate model generalization and adaptability to images from unseen domains. <strong>Project page:</strong> <span><span>https://noseefood.github.io/us-speckle2self/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103755"},"PeriodicalIF":11.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860859","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":"Exploring the robustness of TractOracle methods in RL-based tractography","authors":"Jeremi Levesque,&nbsp;Antoine Théberge,&nbsp;Maxime Descoteaux,&nbsp;Pierre-Marc Jodoin","doi":"10.1016/j.media.2025.103743","DOIUrl":"10.1016/j.media.2025.103743","url":null,"abstract":"<div><div>Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain’s white matter. Among machine learning approaches, reinforcement learning (RL) has emerged as a promising framework for tractography, outperforming traditional methods in several key aspects. TractOracle-RL, a recent RL-based approach, reduces false positives by incorporating anatomical priors into the training process via a reward-based mechanism.</div><div>In this paper, we investigate four extensions of the original TractOracle-RL framework by integrating recent advances in RL, and we evaluate their performance across five diverse diffusion MRI datasets. Results demonstrate that combining an oracle with the RL framework consistently leads to robust and reliable tractography, regardless of the specific method or dataset used.</div><div>We also introduce a novel RL training scheme called <em>Iterative Reward Training (IRT)</em>, inspired by the Reinforcement Learning from Human Feedback (RLHF) paradigm. Instead of relying on human input, IRT leverages bundle filtering methods to iteratively refine the oracle’s guidance throughout training. Experimental results show that RL methods trained with oracle feedback significantly outperform widely used tractography techniques in terms of accuracy and anatomical validity.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103743"},"PeriodicalIF":11.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852185","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":"Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations","authors":"Daan Kuppens ,&nbsp;Sebastiano Barbieri ,&nbsp;Daisy van den Berg ,&nbsp;Pepijn Schouten ,&nbsp;Harriet C. Thoeny ,&nbsp;Hanneke W.M. van Laarhoven ,&nbsp;Myrte Wennen ,&nbsp;Oliver J. Gurney-Champion","doi":"10.1016/j.media.2025.103768","DOIUrl":"10.1016/j.media.2025.103768","url":null,"abstract":"<div><div>Deep learning has proven to be a suitable alternative to least squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR acquisition protocols. In practice, QMRI acquisition protocols differ substantially between different studies and clinical settings. The lack of generalizability and adoptability of current deep learning approaches for QMRI parameter estimation impedes the implementation of these algorithms in clinical trials and clinical practice. Neural Controlled Differential Equations (NCDEs) allow for the sampling of incomplete and irregularly sampled data with variable length, making them ideal for use in QMRI parameter estimation. In this study, we show that NCDEs can function as a generic tool for the accurate estimation of QMRI parameters, regardless of QMRI sequence length, configuration of independent variables and QMRI forward model (variable flip angle <em>T1</em>-mapping, intravoxel incoherent motion MRI, dynamic contrast-enhanced MRI). NCDEs achieved lower mean squared error than LSQ fitting in low-SNR simulations and in vivo in challenging anatomical regions like the abdomen and leg, but this improvement was no longer evident at high SNR. When NCDEs improve parameter estimation, they tend to do so by reducing the variance in estimation errors. These findings suggest that NCDEs offer a robust approach for reliable QMRI parameter estimation, especially in scenarios with high uncertainty or low image quality. We believe that with NCDEs, we have solved one of the main challenges for using deep learning for QMRI parameter estimation in a broader clinical and research setting.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103768"},"PeriodicalIF":11.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899217","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":"Towards cardiac MRI foundation models: Comprehensive visual-tabular representations for whole-heart assessment and beyond","authors":"Yundi Zhang ,&nbsp;Paul Hager ,&nbsp;Che Liu ,&nbsp;Suprosanna Shit ,&nbsp;Chen Chen ,&nbsp;Daniel Rueckert ,&nbsp;Jiazhen Pan","doi":"10.1016/j.media.2025.103756","DOIUrl":"10.1016/j.media.2025.103756","url":null,"abstract":"<div><div>Cardiac magnetic resonance (CMR) imaging is the gold standard for non-invasive cardiac assessment, offering rich spatio-temporal views of the heart’s anatomy and physiology. Patient-level health factors, such as demographics, metabolic, and lifestyle, are known to substantially influence cardiovascular health and disease risk, yet remain uncaptured by CMR alone. To holistically understand cardiac health and to enable the best possible interpretation of an individual’s disease risk, CMR and patient-level factors must be jointly exploited within an integrated framework. Recent multi-modal approaches have begun to bridge this gap, yet they often rely on limited spatio-temporal data and focus on isolated clinical tasks, thereby hindering the development of a comprehensive representation for cardiac/health evaluation.</div><div>To overcome these limitations, we introduce <em>ViTa</em>, a step toward foundation models that delivers a comprehensive representation of the heart and a precise interpretation of individual disease risk. Leveraging data from 42,000 UK Biobank participants, ViTa integrates 3D+T cine stacks from short-axis and long-axis views, enabling a complete capture of the cardiac cycle. These imaging data are then fused with detailed tabular patient-level factors, enabling context-aware insights. This multi-modal paradigm supports a wide spectrum of downstream tasks, including cardiac phenotype and physiological feature prediction, segmentation, and classification of cardiac/metabolic diseases within a single unified framework. By learning a shared latent representation that bridges rich imaging features and patient context, ViTa moves beyond traditional, task-specific models toward a universal, patient-specific understanding of cardiac health, highlighting its potential to advance clinical utility and scalability in cardiac analysis. <span><span>²</span></span></div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103756"},"PeriodicalIF":11.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852186","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":"Deep generation of personalized connectomes based on individual attributes","authors":"Yuanzhe Liu ,&nbsp;Caio Seguin ,&nbsp;Sina Mansour L․ ,&nbsp;Ye Ella Tian ,&nbsp;Maria A. Di Biase ,&nbsp;Andrew Zalesky","doi":"10.1016/j.media.2025.103761","DOIUrl":"10.1016/j.media.2025.103761","url":null,"abstract":"<div><div>An individual’s connectome is unique. Interindividual variation in connectome architecture associates with disease status, cognition, lifestyle factors, and other personal attributes. While models to predict personal attributes from a person’s connectome are abundant, the inverse task—inferring connectome architecture from an individual’s personal profile—has not been widely studied. Here, we introduce a deep model to generate a person’s entire connectome exclusively based on their age, sex, body phenotypes, cognition, and lifestyle factors. Using the richly phenotyped UK Biobank connectome cohort (N=8,086), we demonstrate that our model can generate network architectures that closely recapitulate connectomes mapped empirically using diffusion MRI and tractography. We find that age, sex, and body phenotypes exert the strongest influence on the connectome generation process, with an impact approximately four times greater than that of cognition and lifestyle factors. Regional differences in the importance of measures were observed, including an increased importance of cognition in the association cortex relative to the visual system. We further show that generated connectomes can improve the training of machine learning models and reduce their predictive errors. Our work demonstrates the feasibility of inferring brain connectivity from an individual’s personal data and enables future applications of connectome generation such as data augmentation and anonymous data sharing.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103761"},"PeriodicalIF":11.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829842","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":"OCELOT 2023: Cell detection from cell–tissue interaction challenge","authors":"JaeWoong Shin ,&nbsp;Jeongun Ryu ,&nbsp;Aaron Valero Puche ,&nbsp;Jinhee Lee ,&nbsp;Biagio Brattoli ,&nbsp;Wonkyung Jung ,&nbsp;Soo Ick Cho ,&nbsp;Kyunghyun Paeng ,&nbsp;Chan-Young Ock ,&nbsp;Donggeun Yoo ,&nbsp;Zhaoyang Li ,&nbsp;Wangkai Li ,&nbsp;Huayu Mai ,&nbsp;Joshua Millward ,&nbsp;Zhen He ,&nbsp;Aiden Nibali ,&nbsp;Lydia Anette Schoenpflug ,&nbsp;Viktor Hendrik Koelzer ,&nbsp;Xu Shuoyu ,&nbsp;Ji Zheng ,&nbsp;Sérgio Pereira","doi":"10.1016/j.media.2025.103751","DOIUrl":"10.1016/j.media.2025.103751","url":null,"abstract":"<div><div>Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. However, existing deep learning-based cell detection models struggle to replicate these behaviors and learn the interdependent semantics between structures at different magnifications. A key barrier in the field is the lack of datasets with multi-scale overlapping cell and tissue annotations. The OCELOT 2023 challenge was initiated to gather insights from the community to validate the hypothesis that understanding cell and tissue (cell–tissue) interactions is crucial for achieving human-level performance, and to accelerate the research in this field. The challenge dataset includes overlapping cell detection and tissue segmentation annotations from six organs, comprising 673 pairs sourced from 306 The Cancer Genome Atlas (TCGA) Whole-Slide Images with hematoxylin and eosin staining, divided into training, validation, and test subsets. Participants presented models that significantly enhanced the understanding of cell–tissue relationships. Top entries achieved up to a 7.99 increase in F1-score on the test set compared to the baseline cell-only model that did not incorporate cell–tissue relationships. This is a substantial improvement in performance over traditional cell-only detection methods, demonstrating the need for incorporating multi-scale semantics into the models. This paper provides a comparative analysis of the methods used by participants, highlighting innovative strategies implemented in the OCELOT 2023 challenge.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103751"},"PeriodicalIF":11.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812184","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":"DMP-Net: Deep semantic prior compressed spectral reconstruction method towards intraoperative imaging of brain tissue","authors":"Chipeng Cao ,&nbsp;Jie Li ,&nbsp;Pan Wang ,&nbsp;Chun Qi","doi":"10.1016/j.media.2025.103750","DOIUrl":"10.1016/j.media.2025.103750","url":null,"abstract":"<div><div>In the diagnosis and surgical resection of brain tumors, hyperspectral imaging, as a non-invasive detection technology, can effectively characterize the morphological structure and the physicochemical differences in cellular metabolism of different tissues. However, live tissues typically exhibit certain motion characteristics, and traditional hyperspectral imaging systems struggle to meet the demands for real-time and rapid imaging. The snapshot compressive spectral imaging (CSI) system can quickly acquire spatial spectral information of the lesion area in a single exposure and, combined with reconstruction algorithms, effectively restore the high-dimensional spectral information of brain tissue. High-quality reconstruction results are crucial for ensuring the reliability of spectral analysis of brain tissue. To improve the reconstruction performance of the CSI system, this paper proposes a compressive spectral reconstruction method based on deep semantic prior regularization. The predicted results of the deep convolutional prior model are used as the initial spectral estimate to establish a regularization term for the reconstruction process. This is combined with the Alternating Direction Method of Multipliers (ADMM) to optimize the solution for high-dimensional spectral images of brain tissue. The results show that using the CSI system for intraoperative brain tissue imaging can rapidly acquire spatial spectral information of the lesion area. By optimizing the reconstruction process with the deep convolutional prior model, this method not only better preserves the structural consistency of spectral images from different patients but also fully considers the spectral differences of different types of brain tumors, achieving higher reconstruction quality. This provides strong support for the precise localization and resection of brain tumors. The source code and related data of the proposed method can be downloaded at <span><span>https://github.com/ccp1025/DMP-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103750"},"PeriodicalIF":11.8,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828087","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":"CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment","authors":"Yulong Dou ,&nbsp;Han Wu ,&nbsp;Changjian Li ,&nbsp;Chen Wang ,&nbsp;Tong Yang ,&nbsp;Min Zhu ,&nbsp;Dinggang Shen ,&nbsp;Zhiming Cui","doi":"10.1016/j.media.2025.103746","DOIUrl":"10.1016/j.media.2025.103746","url":null,"abstract":"<div><div>Traditional semi-automatic methods for tooth alignment involve laborious manual procedures and heavily depend on the expertise of dentists, which often leads to inefficient and prolonged treatment durations. Although many automatic methods have been proposed to assist especially the less experienced dentists, they often lack incorporating clinical insight and oversimplify the problem by estimating rigid transformation matrix for each tooth directly from dental point clouds. This over-simplification fails to capture nuanced requirements of orthodontic treatment, i.e., specific clinical rules for effective alignment of misaligned teeth. To address this, we propose CLIK-Diffusion, a <u>CLI</u>nical <u>K</u>nowledge-informed <u>Diffusion</u> model for automatic tooth alignment. CLIK-Diffusion formulates the complex problem of tooth alignment as a more manageable landmark transformation problem, which is further refined into a landmark coordinate generation task. Specifically, we first detect landmarks for each tooth by category, and then build our CLIK-Diffusion to learn distribution of normal occlusion. To further encourage the integration of essential clinical knowledge, we design hierarchical constraints from three perspectives: (1) dental-arch level: to constrain arrangement of teeth from a global level; (2) inter-tooth level: to ensure tight contact and avoid unnecessary collision between neighboring teeth; and (3) individual-tooth level: to guarantee correct orientation of each tooth. In this way, our designed CLIK-Diffusion is able to predict the post-orthodontic landmarks that align with clinical knowledge, and then estimate rigid transformation for each tooth based on coordinates of its pre- and post-orthodontic landmarks. We have evaluated our CLIK-Diffusion on various malocclusion cases collected in real-world clinics, and demonstrate its exceptional performance and strong applicability in orthodontic treatment, compared with other state-of-the-art methods. Our dataset and code is available at <span><span>https://github.com/ShanghaiTech-IMPACT/CLIK-Diffusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103746"},"PeriodicalIF":11.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772637","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}