Medical image analysis最新文献

{"title":"IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry","authors":"Jieyun Bai ,&nbsp;Yitong Tang ,&nbsp;Xiao Liu ,&nbsp;Jiale Hu ,&nbsp;Yunda Li ,&nbsp;Xufan Chen ,&nbsp;Yufeng Wang ,&nbsp;Chen Ma ,&nbsp;Yunshu Li ,&nbsp;Bowen Guo ,&nbsp;Jing Jiao ,&nbsp;Yi Huang ,&nbsp;Kun Wang ,&nbsp;Lifei Li ,&nbsp;Yuzhang Ma ,&nbsp;Xiaoxin Han ,&nbsp;Haochen Shao ,&nbsp;Zi Yang ,&nbsp;Qingchen Liu ,&nbsp;Yuchen Hu ,&nbsp;Shuo Li","doi":"10.1016/j.media.2026.103960","DOIUrl":"10.1016/j.media.2026.103960","url":null,"abstract":"<div><div>Accurate intrapartum biometry plays a crucial role in monitoring labor progression and preventing complications. However, its clinical application is limited by challenges such as the difficulty in identifying anatomical landmarks and the variability introduced by operator dependency. To overcome these challenges, the Intrapartum Ultrasound Grand Challenge (IUGC) 2025, in collaboration with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), was organized to accelerate the development of automatic measurement techniques for intrapartum ultrasound analysis. The challenge featured a large-scale, multi-center dataset comprising over 32,000 images from 24 hospitals and research institutes. These images were annotated with key anatomical landmarks of the pubic symphysis (PS) and fetal head (FH), along with the corresponding biometric parameter-the angle of progression (AoP). Ten participating teams proposed a variety of end-to-end and semi-supervised frameworks, incorporating advanced strategies such as foundation model distillation, pseudo-label refinement, anatomical segmentation guidance, and ensemble learning. A comprehensive evaluation revealed that the winning team achieved superior accuracy, with a Mean Radial Error (MRE) of 6.53 ± 4.38 pixels for the right PS landmark, 8.60 ± 5.06 pixels for the left PS landmark, 19.90 ± 17.55 pixels for the FH tangent landmark, and an absolute AoP difference of 3.81 ± 3.12° This top-performing method demonstrated accuracy comparable to expert sonographers, emphasizing the clinical potential of automated intrapartum ultrasound analysis. However, challenges remain, such as the trade-off between accuracy and computational efficiency, the lack of segmentation labels and video data, and the need for extensive multi-center clinical validation. IUGC 2025 thus sets the first benchmark for landmark-based intrapartum biometry estimation and provides an open platform for developing and evaluating real-time, intelligent ultrasound analysis solutions for labor management.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103960"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033892","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":"Robust non-rigid image-to-patient registration for contactless dynamic thoracic tumor localization using recursive deformable diffusion models","authors":"Dongyuan Li ,&nbsp;Yixin Shan ,&nbsp;Yuxuan Mao ,&nbsp;Puxun Tu ,&nbsp;Haochen Shi ,&nbsp;Shenghao Huang ,&nbsp;Weiyan Sun ,&nbsp;Chang Chen ,&nbsp;Xiaojun Chen","doi":"10.1016/j.media.2026.103948","DOIUrl":"10.1016/j.media.2026.103948","url":null,"abstract":"<div><div>Deformable image-to-patient registration is essential for surgical navigation and medical imaging, yet real-time computation of spatial transformations across modalities remains a major clinical challenge-often being time-consuming, error-prone, and potentially increasing trauma or radiation exposure. While state-of-the-art methods achieve impressive speed and accuracy on paired medical images, they face notable limitations in cross-modal thoracic applications, where physiological motions such as respiration complicate tumor localization. To address this, we propose a robust, contactless, non-rigid registration framework for dynamic thoracic tumor localization. A highly efficient Recursive Deformable Diffusion Model (RDDM) is trained to reconstruct comprehensive 4DCT sequences from only end-inhalation and end-exhalation scans, capturing respiratory dynamics reflective of the intraoperative state. For real-time patient alignment, we introduce a contactless non-rigid registration algorithm based on GICP, leveraging patient skin surface point clouds captured by stereo RGB-D imaging. By incorporating normal vector and expansion-contraction constraints, the method enhances robustness and avoids local minima. The proposed framework was validated on publicly available datasets and volunteer trials. Quantitative evaluations demonstrated the RDDM’s anatomical fidelity across respiratory phases, achieving an PSNR of 34.01 ± 2.78 dB. Moreover, we have preliminarily developed a 4DCT-based registration and surgical navigation module to support tumor localization and high-precision tracking. Experimental results indicate that the proposed framework preliminarily meets clinical requirements and demonstrates potential for integration into downstream surgical systems.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103948"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956932","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":"Generating synthetic MRI scans for improving Alzheimer’s disease diagnosis","authors":"Rosanna Turrisi,&nbsp;Giuseppe Patané","doi":"10.1016/j.media.2026.103947","DOIUrl":"10.1016/j.media.2026.103947","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with Machine Learning (ML) enables early diagnosis, but ML models often underperform when trained on small, heterogeneous medical datasets. Transfer Learning (TL) helps mitigate this limitation, yet models pre-trained on 2D natural images still fall short of those trained directly on related 3D MRI data. To address this gap, we introduce an intermediate strategy based on synthetic data generation. Specifically, we propose a conditional Denoising Diffusion Probabilistic Model (DDPM) to synthesise 2D projections (axial, coronal, sagittal) of brain MRI scans across three clinical groups: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. A total of 9000 synthetic images are used for pre-training 2D models, which are subsequently extended to 3D via axial, coronal, and sagittal convolutions and fine-tuned on real-world small datasets. Our method achieves 91.3% accuracy in binary (CN vs. AD) and 74.5% in three-class (CN/MCI/AD) classification on the 3T ADNI dataset, outperforming both models trained from scratch and those pre-trained on ImageNet. Our 2D ADnet achieved state-of-the-art performance on OASIS-2 (59.3% accuracy, 57.6% F1), surpassing all competitor models and confirming the robustness of synthetic data pre-training. These results show synthetic diffusion-based pre-training as a promising bridge between natural image TL and medical MRI data.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103947"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032814","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":"Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation","authors":"Chengjin Yu ,&nbsp;Bin Zhang ,&nbsp;Chenchu Xu ,&nbsp;Dongsheng Ruan ,&nbsp;Rui Wang ,&nbsp;Huafeng Liu ,&nbsp;Xiaohu Li ,&nbsp;Shuo Li","doi":"10.1016/j.media.2026.103971","DOIUrl":"10.1016/j.media.2026.103971","url":null,"abstract":"<div><div>Masked Autoencoders (MAEs) have grown increasingly prominent as a powerful self-supervised learning paradigm. They are capable of effectively leveraging inherent image prior information and are gaining traction in the field of medical image analysis. However, their application to feature representations of the non-salient objects, such as microvasculature, accessory organs, and early-stage tumors–is fundamentally limited by dimensional collapse problem, which diminishes feature diversity critical for non-salient structure discrimination. To address this, we propose a Multi-Granularity Masked Autoencoder (MG-MAE) framework for feature diversity learning: (1) We extend the conventional MAE into a multi-granularity framework, a global branch reconstructs global pixels, with a local branch recovering Histogram of Oriented Gradients (HOG) features, enabling hierarchical representation of both coarse-grained and fine-grained patterns; (2) Critically, in the local branch, a diversity-enhanced loss function incorporating Nuclear Norm Maximization (NNM) constraint to explicitly mitigate feature space collapse through orthogonal embedding regularization; and (3) A Dynamic Weight Adjustment (DWA) strategy that dynamically prioritizes hard-to-reconstruct regions via entropy-driven gradient modulation. Comprehensive evaluations across five clinical benchmarks–CCTA139, BTCV, LiTS, ACDC, and MSD Pancreas Tumour datasets–demonstrate that MG-MAE achieves statistically significant improvements in Dice Similarity Coefficient (DSC) scores for non-salient object segmentation, outperforming state-of-the-art methods. The code is available at <span><span>https://github.com/zhangbbin/mgmae</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103971"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134049","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":"DSFNet: Dual-source and spatiotemporal-feature fusion network for bedside diagnosis of lung injuries with electrical impedance tomography","authors":"Zhiwei Li ,&nbsp;Yang Wu ,&nbsp;Kai Liu ,&nbsp;Yingqi Zhang ,&nbsp;Bai Chen ,&nbsp;Hao Wang ,&nbsp;Jiafeng Yao","doi":"10.1016/j.media.2026.104003","DOIUrl":"10.1016/j.media.2026.104003","url":null,"abstract":"<div><div>Electrical Impedance Tomography (EIT) is a promising tool for non-invasive and real-time lung monitoring, but the data heterogeneity and low spatial resolution limit its ability to diagnose lung injuries. To address these challenges, we propose DSFNet, a dual-source and spatiotemporal-feature fusion network that integrates EIT spatiotemporal boundary voltages and ventilation images to classify four lung conditions, including healthy (HE), pneumothorax (PN), pleural effusion (PE), and pneumonia (PM). The temporal dynamics modeling (TDM) module and multi-head self-attention (MHSA) module are designed to improve the temporal feature extraction and representation of DSFNet. We construct a novel EIT simulation dataset describing pathological respiratory patterns and introduce a hybrid data learning strategy that combines simulation data (SD) and experimental data (ED) to address the small sample problem and improve the accuracy of model classification. The DSFNet trained with the SD + 25 % ED pattern achieved an accuracy of 97.78 % and 96.55 % on the dynamic phantom dataset and the clinical human dataset, respectively, demonstrating its excellent performance and robustness. The SHAP analysis further revealed the feature contributions of the input data. This study provides an effective approach for bedside lung injury diagnosis based on multi-source EIT data.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 104003"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777317","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":"Multimodal sparse fusion transformer network with spatio-temporal decoupling for breast tumor classification","authors":"Jiahao Xu ,&nbsp;Shuxin Zhuang ,&nbsp;Yi He ,&nbsp;Haolin Wang ,&nbsp;Zhemin Zhuang ,&nbsp;Huancheng Zeng","doi":"10.1016/j.media.2026.103966","DOIUrl":"10.1016/j.media.2026.103966","url":null,"abstract":"<div><div>Accurate analysis of tumor morphology, vascularity, and tissue stiffness under multimodal ultrasound imaging plays a critical role in the diagnosis of breast cancer. However, manual interpretation across multiple modalities is time-consuming and heavily dependent on the radiologist’s expertise. Computer-aided classification offers an efficient alternative, yet remains challenging due to significant modality heterogeneity, inconsistent image quality, and redundant information across modalities. To address these issues, we propose a novel Multimodal Sparse Fusion Transformer Network (MSFT-Net). First, a Spatio-Temporal Decoupling Attention architecture (STDA) is introduced to disentangle and extract dynamic and static features from different modalities along spatial and temporal dimensions, capturing modality-specific motion and morphological characteristics independently. Second, the Mixed-Scale Convolution Module (MSCM) obtains tumor features at multiple scales, enhancing geometric detail representation and improving receptive field coverage. Third, the Sparse Cross-Attention Module (SCAM) adaptively retains the most effective query-key interactions between modalities, thereby facilitating the aggregation of high-quality features for robust multimodal information fusion. MSFT-Net is trained and tested on a curated dataset comprising multimodal breast tumor videos collected from 458 patients, including ultrasound (US), superb microvascular imaging (SMI), and strain elastography (SE), and its generalizability is further validated on the public BraTS'21 MRI dataset. Extensive experiments demonstrate that MSFT-Net achieves superior performance in multimodal breast tumor classification compared to state-of-the-art methods, providing fast and reliable support for radiologists in diagnostic tasks.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103966"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072192","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":"Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets","authors":"Nicholas Konz ,&nbsp;Richard Osuala ,&nbsp;Preeti Verma ,&nbsp;Yuwen Chen ,&nbsp;Hanxue Gu ,&nbsp;Haoyu Dong ,&nbsp;Yaqian Chen ,&nbsp;Andrew Marshall ,&nbsp;Lidia Garrucho ,&nbsp;Kaisar Kushibar ,&nbsp;Daniel M. Lang ,&nbsp;Gene S. Kim ,&nbsp;Lars J. Grimm ,&nbsp;John M. Lewin ,&nbsp;James S. Duncan ,&nbsp;Julia A. Schnabel ,&nbsp;Oliver Diaz ,&nbsp;Karim Lekadir ,&nbsp;Maciej A. Mazurowski","doi":"10.1016/j.media.2026.103943","DOIUrl":"10.1016/j.media.2026.103943","url":null,"abstract":"<div><div>Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (<em>e.g.</em>, Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensitivity to image corruptions and adversarial attacks, feature interpretability, and correlation with radiologist-perceived image quality. Additionally, we address key gaps in the literature by presenting an extensive framework for the multifaceted evaluation of image similarity metrics in medical imaging—including the first large-scale comparative study of generative models for medical image translation—and release an accessible codebase to facilitate future research. Our results are supported by thorough experiments spanning a variety of datasets, modalities, and downstream tasks, highlighting the broad potential of FRD for medical image analysis.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103943"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048368","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":"PH2ST: Prompt-guided hypergraph learning for spatial transcriptomics prediction in whole slide images","authors":"Yi Niu ,&nbsp;Jiashuai Liu ,&nbsp;Yingkang Zhan ,&nbsp;Jiangbo Shi ,&nbsp;Di Zhang ,&nbsp;Marika Reinius ,&nbsp;Ines Machado ,&nbsp;Mireia Crispin-Ortuzar ,&nbsp;Jialun Wu ,&nbsp;Chen Li ,&nbsp;Zeyu Gao","doi":"10.1016/j.media.2026.104008","DOIUrl":"10.1016/j.media.2026.104008","url":null,"abstract":"<div><div>Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of current ST technologies restrict their widespread use in clinical and research settings, making obtaining high-resolution transcriptomic profiles across large tissue areas challenging. Predicting ST from H&amp;E-stained histology images has emerged as a promising alternative to address these limitations but remains challenging due to the heterogeneous relationship between histomorphology and gene expression, which is affected by substantial variability across patients and tissue sections. In response, we propose PH2ST, a prompt-guided hypergraph learning framework, which leverages limited ST signals to guide multi-scale histological representation learning for accurate and robust spatial gene expression prediction. Extensive evaluations on two public ST datasets and multiple prompt sampling strategies simulating real-world scenarios demonstrate that PH2ST not only outperforms existing state-of-the-art methods, but also shows strong potential for practical applications such as imputing missing spots, ST super-resolution, and local-to-global prediction, highlighting its value for scalable and cost-effective spatial gene expression mapping in biomedical contexts.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 104008"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147334544","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":"Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction","authors":"Jef Jonkers ,&nbsp;Frank Coopman ,&nbsp;Luc Duchateau ,&nbsp;Glenn Van Wallendael ,&nbsp;Sofie Van Hoecke","doi":"10.1016/j.media.2026.103953","DOIUrl":"10.1016/j.media.2026.103953","url":null,"abstract":"<div><div>Automatic anatomical landmark localization in medical imaging requires not just accurate predictions but reliable uncertainty quantification for effective clinical decision support. Current uncertainty quantification approaches often fall short, particularly when combined with normality assumptions, systematically underestimating total predictive uncertainty. This paper introduces conformal prediction as a framework for reliable uncertainty quantification in anatomical landmark localization, addressing a critical gap in automatic landmark localization. We present two novel approaches guaranteeing finite-sample validity for multi-output prediction: multi-output regression-as-classification conformal prediction (M-R2CCP) and its variant multi-output regression to classification conformal prediction set to region (M-R2C2R). Unlike conventional methods that produce axis-aligned hyperrectangular or ellipsoidal regions, our approaches generate flexible, non-convex prediction regions that better capture the underlying uncertainty structure of landmark predictions. Through extensive empirical evaluation across multiple 2D and 3D datasets, we demonstrate that our methods consistently outperform existing multi-output conformal prediction approaches in both validity and efficiency. This work represents a significant advancement in reliable uncertainty estimation for anatomical landmark localization, providing clinicians with trustworthy confidence measures for their diagnoses. While developed for medical imaging, these methods show promise for broader applications in multi-output regression problems.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103953"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071492","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":"AtlasMorph: Learning conditional deformable templates for brain MRI","authors":"Marianne Rakic ,&nbsp;Andrew Hoopes ,&nbsp;Mazdak S. Abulnaga ,&nbsp;Mert R. Sabuncu ,&nbsp;John V. Guttag ,&nbsp;Adrian V. Dalca ,&nbsp;for the Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1016/j.media.2025.103893","DOIUrl":"10.1016/j.media.2025.103893","url":null,"abstract":"<div><div>Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies and computational anatomy tasks such as registration and segmentation. Because developing a template is a computationally expensive process, relatively few templates are available. As a result, analysis is often conducted with sub-optimal templates that are not truly representative of the study population, especially when there are large variations within this population.</div><div>We propose a machine learning framework that uses convolutional registration neural networks to efficiently learn a function that outputs templates conditioned on subject-specific attributes, such as age and sex. We also leverage segmentations, when available, to produce anatomical segmentation maps for the resulting templates. The learned network can also be used to register subject images to the templates. We demonstrate our method on a compilation of 3D brain MRI datasets, and show that it can learn high-quality templates that are representative of populations. We find that annotated conditional templates enable better registration than their unlabeled unconditional counterparts, and outperform other templates construction methods.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103893"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731917","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}