Medical image analysis最新文献

{"title":"MIRAGE: Medical image-text pre-training for robustness against noisy environments","authors":"Pujin Cheng ,&nbsp;Yijin Huang ,&nbsp;Li Lin ,&nbsp;Junyan Lyu ,&nbsp;Kenneth Kin-Yip Wong ,&nbsp;Xiaoying Tang","doi":"10.1016/j.media.2025.103912","DOIUrl":"10.1016/j.media.2025.103912","url":null,"abstract":"<div><div>Contrastive vision-language pre-training models have achieved significant success on large-scale general multi-modality datasets. However, in the medical domain, the high costs of data collection and expert annotation are likely to result in small-sized and noisy datasets, which can severely limit model performance due to overfitting unreliable data and misrepresenting patterns. To address this challenge, we present MIRAGE, a novel framework designed to handle mismatched false positives and semantically related false negatives during medical image-text pre-training. Cross-entropy-based optimization proves inadequate for noisy contrastive settings, as it tends to fail in distinguishing noisy samples and ends up fitting them, leading to suboptimal representations. To overcome this limitation, we introduce an optimal transport-based contrastive loss that effectively identifies noisy samples leveraging the nearest cross-modality neighbor prior, thereby reducing noisy samples’ adverse impact. Additionally, we propose an adaptive gradient balancing strategy that mitigates the influence of gradients from noisy samples. Extensive experiments demonstrate that MIRAGE achieves superior performance across six tasks and 14 datasets, largely outperforming representative state-of-the-art methods. Furthermore, comprehensive analyses on synthetic noisy data are performed, clearly demonstrating the contribution of each component in MIRAGE.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103912"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731918","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":"UDV-Net: A hybrid CNN and transformer vein segmentation network with vascular prior and spatial awareness","authors":"Bowei Shen ,&nbsp;Xiaoquan Huang ,&nbsp;Yuli Li ,&nbsp;Xinghuan Li ,&nbsp;Lili Ma ,&nbsp;Yonghong Shi ,&nbsp;Shiyao Chen","doi":"10.1016/j.media.2025.103929","DOIUrl":"10.1016/j.media.2025.103929","url":null,"abstract":"<div><div>CNNs handling multi-scale variations and Transformers modeling long-range dependencies are crucial for vascular segmentation. The fusion of these two models effectively combines the multi-scale local features extracted by CNNs with the global information modeled by Transformers, significantly enhancing the accuracy of blood vessel segmentation. However, the powerful model faces challenges when dealing with the gradual formation of extensive collateral vessels in the upper digestive system veins of patients with cirrhotic portal hypertension, leading to numerous false negative and false positive segmentation results. To this end, the paper proposes UDV-Net, a fusion network combining CNN and Transformer with vessel prior and spatial awareness for upper digestive system vein vessel segmentation. Initially, a CNN utilizing an encoding-decoding architecture is employed to create a multi-scale representation of blood vessels from the image. The representation is further refined by the blood vessel attention module at the corresponding scale to address tubular structures, thereby reducing false positive results. Secondly, a Transformer bridge with three-dimensional voxel position encoding is proposed to connect the corresponding encoding-decoding layer, effectively perceiving widely distributed blood vessels with diverse shapes, improving blood vessel connectivity, and avoiding false negative blood vessel results. We collected and annotated abdominal contrast-enhanced CT images of 191 patients with liver cirrhosis, constituting the PHCT dataset. Our method’s validation result on this dataset is state-of-the-art. When evaluated on the publicly available 3D-IRCADb dataset as an unseen external validation set for PHCT, the model demonstrated satisfactory performance. Additionally, our method also achieves the optimal performance on the public MSD hepatic vessel dataset.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103929"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893999","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":"DVAP-Reg: Dual-view anatomical prior-driven cross-dimensional registration for spinal surgery navigation","authors":"Zhengyang Wu ,&nbsp;Wenjie Zheng ,&nbsp;Yingjie Hao ,&nbsp;Jing Ling ,&nbsp;Maodan Nie ,&nbsp;Rui Zuo ,&nbsp;Minghan Liu ,&nbsp;Zegang Shi ,&nbsp;Wen Xia ,&nbsp;Fayuan Zhou ,&nbsp;Zhuojun Cao ,&nbsp;Jingjing Xiao ,&nbsp;Weisheng Li ,&nbsp;Guifeng Xia ,&nbsp;Changqing Li ,&nbsp;Yucheng Shu ,&nbsp;Chao Zhang","doi":"10.1016/j.media.2025.103930","DOIUrl":"10.1016/j.media.2025.103930","url":null,"abstract":"<div><div>2D-3D cross-dimensional registration serves as a critical technology in spinal surgery navigation, with profound implications for enhancing surgical precision, reducing radiation exposure and mitigating surgical risks. Its core objective is to achieve visual navigation by accurately aligning preoperative high-resolution 3D vertebrae with intraoperative 2D X-rays. However, this technology remains constrained by prominent challenges, primarily arising from the inherent semantic and dimensional discrepancies. Traditional registration methods, typically relying on classical iterative optimization strategies, suffer from low computational efficiency. Meanwhile, deep learning-based approaches struggle to accommodate the spatial randomness inherent in intraoperative 2D X-rays and 3D vertebrae. In this paper, we propose a dual-view anatomical prior-driven cross-dimensional registration method for spinal surgery navigation. First, a direct regression network based on dual-view X-rays, integrated with a proposed spatial correlation mechanism, is employed to enhance geometric consistency constraints and mitigate inter-patient anatomical variability. Then, corresponding vertebrae’s anatomical priors, extracted via the proposed Face-GCN module as conditional information enhancement for high-level, generalized spatial perception, are fused into the regression network for spatial pose alignment guidance. Finally, a clinical cross-dimensional image dataset is released using the developed interactive registration platform. The proposed network has been validated in real-world spinal surgical navigation scenarios across diverse lumbar spine pathologies, utilizing authentic intraoperative 2D X-rays and preoperative 3D CT images. In these clinical settings, our method achieves real-time 2D-3D image-assisted navigation with rotational and translational accuracy of 2.81° and 1.82 mm, demonstrating its ability to keep pace with the time-sensitive, ever-changing nature of intraoperative workflows. Our project’s dataset and source code are available at: <span><span>https://github.com/TMMU-KLPOP/DVAP-Reg</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103930"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895637","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":"AGFS-tractometry: A novel atlas-guided fine-scale tractometry approach for enhanced along-tract group statistical comparison using diffusion MRI tractography","authors":"Ruixi Zheng ,&nbsp;Wei Zhang ,&nbsp;Yijie Li ,&nbsp;Xi Zhu ,&nbsp;Zhou Lan ,&nbsp;Jarrett Rushmore ,&nbsp;Yogesh Rathi ,&nbsp;Nikos Makris ,&nbsp;Lauren J. O’Donnell ,&nbsp;Fan Zhang","doi":"10.1016/j.media.2025.103892","DOIUrl":"10.1016/j.media.2025.103892","url":null,"abstract":"<div><div>Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain’s white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has become an essential tool for studying local along-tract differences between different populations (e.g., health vs disease). In this study, we propose a novel atlas-guided fine-scale tractometry method, namely AGFS-Tractometry, that leverages tract spatial information and permutation testing to enhance the along-tract statistical analysis between populations. There are two major contributions in AGFS-Tractometry. First, we create a novel atlas-guided tract profiling template that enables consistent, fine-scale, along-tract parcellation of subject-specific fiber tracts. Second, we propose a novel nonparametric permutation testing group comparison method to enable simultaneous analysis across all along-tract parcels while correcting for multiple comparisons. We perform experimental evaluations on synthetic datasets with known group differences and in vivo real data. We compare AGFS-Tractometry with two state-of-the-art tractometry methods, including Automated Fiber-tract Quantification (AFQ) and BUndle ANalytics (BUAN). Our results show that the proposed AGFS-Tractometry obtains enhanced sensitivity and specificity in detecting local WM differences. In the real data analysis experiments, AGFS-Tractometry can identify more regions with significant differences, which are anatomically consistent with the existing literature. Overall, these demonstrate the ability of AGFS-Tractometry to detect subtle or spatially localized WM group-level differences. The created tract profiling template and related code are available at: <span><span>https://github.com/ZhengRuixi/AGFS-Tractometry.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103892"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689531","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":"FDA-Recon: Feature and data alignment reconstruction for sparse-view CBCT","authors":"Yikun Zhang ,&nbsp;Yao Wang ,&nbsp;Xian Wu ,&nbsp;Dianlin Hu ,&nbsp;Tianling Lyu ,&nbsp;Yan Xi ,&nbsp;Xu Ji ,&nbsp;Jian Yang ,&nbsp;Yang Chen","doi":"10.1016/j.media.2025.103928","DOIUrl":"10.1016/j.media.2025.103928","url":null,"abstract":"<div><div>Cone-beam computed tomography (CBCT) enables real-time three-dimensional imaging for patients, which is of great significance in improving the precision of radiotherapy and interventional procedures. Sparse-view CBCT, which can relax the readout rate of flat-panel detectors and reduce the radiation dose of X-rays, is a promising technology. However, sparse sampling can lead to streak artifacts in the reconstructed images, while the low-power X-ray source of the CBCT scanner can produce low SNR measurements. These degradations hinder accurate guidance for subsequent treatment procedures. To address these issues, this study proposes a learning-based reconstruction algorithm for sparse-view CBCT. To ensure the robustness of the proposed method in real-world scenarios, this study first constructs a large-scale simulated dataset whose distribution is close to the real data based on X-ray imaging physics and CT system characteristics, achieving coarse alignment at the data level. However, simple coarse alignment alone cannot completely bridge the gaps between simulated and real data. Therefore, this study further employs an unsupervised domain adaptation strategy to achieve deeper alignment in the feature space, ensuring the model trained on simulated data maintains its performance on real data. We refer to this strategy as FDA-Recon (Feature and Data Alignment Reconstruction). To achieve high performance in noise suppression and artifact removal, a deep neural network incorporating the novel Vision-LSTM mechanism is developed to fully exploit both local features and global dependencies in images. Results on real data from two different CBCT systems demonstrate the promising performance of the proposed image restoration neural network in artifact removal, noise suppression, and image restoration, as well as the potential of FDA-Recon in addressing sparse-view CBCT reconstruction in practical scenarios.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103928"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894001","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":"Latent diffusion autoencoders: Toward efficient and meaningful unsupervised representation learning in medical imaging - a case study on Alzheimer’s disease","authors":"Gabriele Lozupone ,&nbsp;Alessandro Bria ,&nbsp;Francesco Fontanella ,&nbsp;Frederick J.A. Meijer ,&nbsp;Claudio De Stefano ,&nbsp;Henkjan Huisman","doi":"10.1016/j.media.2026.103932","DOIUrl":"10.1016/j.media.2026.103932","url":null,"abstract":"<div><div>This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer’s disease (AD) using brain MRI from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent representation, improving computational efficiency and making 3D medical imaging representation learning tractable. To validate the proposed approach, we explore two key hypotheses: (i) LDAE effectively captures meaningful semantic representations on 3D brain MRI associated with AD and ageing, and (ii) LDAE achieves high-quality image generation and reconstruction while being computationally efficient. Experimental results support both hypotheses: (i) linear-probe evaluations demonstrate promising diagnostic performance for AD (AUROC: 90%, ACC: 84%) and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic representations enable attribute manipulation, yielding anatomically plausible modifications; (iii) semantic interpolation experiments show strong reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month gap. Even for longer gaps (24 months), the model maintains robust performance (SSIM  &gt;  0.93, MSE  &lt;  0.004), indicating an ability to capture temporal progression trends; (iv) compared to conventional diffusion autoencoders, LDAE significantly increases inference throughput (20 ×  faster) while also enhancing reconstruction quality. These findings position LDAE as a promising framework for scalable medical imaging applications, with the potential to serve as a foundation model for medical image analysis. Code is publicly available at <span><span>https://github.com/GabrieleLozupone/LDAE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103932"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956957","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":"Depth-induced prompt learning for laparoscopic liver landmark detection","authors":"Ruize Cui ,&nbsp;Weixin Si ,&nbsp;Zhixi Li ,&nbsp;Kai Wang ,&nbsp;Jialun Pei ,&nbsp;Pheng-Ann Heng ,&nbsp;Jing Qin","doi":"10.1016/j.media.2026.103940","DOIUrl":"10.1016/j.media.2026.103940","url":null,"abstract":"<div><div>Laparoscopic liver surgery presents a highly intricate intraoperative environment with significant liver deformation, posing challenges for surgeons in locating critical liver structures. Anatomical liver landmarks can greatly assist surgeons in spatial perception in laparoscopic scenarios and facilitate preoperative-to-intraoperative registration. To advance research in liver landmark detection, we develop a new dataset called <em>L3D-2K</em>, comprising 2000 keyframes with expert landmark annotations from surgical videos of 47 patients. Accordingly, we propose a baseline, D²GPLand+, which effectively leverages depth modality to boost landmark detection performance. Concretely, we introduce a Depth-aware Prompt Embedding (DPE) scheme, which dynamically extracts class-related global geometric cues with the guidance of self-supervised prompts from the SAM encoder. Further, a Cross-dimension Unified Mamba (CUMamba) block is designed to comprehensively incorporate RGB and depth features with the concurrent spatial and channel scanning mechanism. Besides, we bring out an Anatomical Feature Augmentation (AFA) module that captures anatomical cues and emphasizes key structures by optimizing feature granularity. For benchmarking purposes, we evaluate our method and 17 mainstream detection models on L3D, L3D-2K, and P2ILF datasets. Experimental results demonstrate that D²GPLand+ obtains superior performance on all three datasets. Our approach provides surgeons with guiding clues that facilitate surgical operations and decision-making in complex laparoscopic surgery. Our code and dataset are available at <span><span>https://github.com/cuiruize/D2GPLand-Plus</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103940"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962447","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":"Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge","authors":"Vladyslav Zalevskyi ,&nbsp;Thomas Sanchez ,&nbsp;Misha Kaandorp ,&nbsp;Margaux Roulet ,&nbsp;Diego Fajardo-Rojas ,&nbsp;Liu Li ,&nbsp;Jana Hutter ,&nbsp;Hongwei Bran Li ,&nbsp;Matthew J. Barkovich ,&nbsp;Hui Ji ,&nbsp;Luca Wilhelmi ,&nbsp;Aline Dändliker ,&nbsp;Céline Steger ,&nbsp;Mériam Koob ,&nbsp;Yvan Gomez ,&nbsp;Anton Jakovčić ,&nbsp;Melita Klaić ,&nbsp;Ana Adžić ,&nbsp;Pavel Marković ,&nbsp;Gracia Grabarić ,&nbsp;Meritxell Bach Cuadra","doi":"10.1016/j.media.2026.103941","DOIUrl":"10.1016/j.media.2026.103941","url":null,"abstract":"<div><div>Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations.</div><div>First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1–2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores.</div><div>Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation.</div><div>Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%.</div><div>Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103941"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995249","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":"GEM-pRF: GPU-empowered mapping of population receptive fields for large-scale fMRI analysis","authors":"Siddharth Mittal,&nbsp;Michael Woletz,&nbsp;David Linhardt,&nbsp;Christian Windischberger","doi":"10.1016/j.media.2025.103891","DOIUrl":"10.1016/j.media.2025.103891","url":null,"abstract":"<div><div>Population receptive field (pRF) mapping is a fundamental technique for understanding retinotopic organisation of the human visual system. Since its introduction in 2008, however, its scalability has been severely hindered by the computational bottleneck of iterative parameter refinement. Current state-of-the-art implementations either sacrifice precision for speed or rely on slow iterative parameter updates, limiting their applicability to large-scale datasets. Here, we present a novel mathematical reformulation of the General Linear Model (GLM), wrapped in a GPU-Empowered Mapping of population Receptive Fields (GEM-pRF) software implementation. By orthogonalizing the design matrix, our approach enables the direct and fast computation of the objective function’s derivatives, which are used to eliminate the iterative refinement process. This approach dramatically accelerates pRF estimation with high accuracy. Validation using empirical and simulated data confirms GEM-pRF’s accuracy, and benchmarking against established tools demonstrates a reduction in computation time of almost two orders of magnitude. With its modular and extensible design, GEM-pRF provides a critical advancement for large-scale fMRI retinotopic mapping. Furthermore, our reformulated GLM approach in combination with GPU-based implementation offers a broadly applicable solution that may extend beyond visual neuroscience, accelerating computational modelling across various domains in neuroimaging and beyond.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103891"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611717","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":"UTMorph: A hybrid CNN-transformer network for weakly-supervised multimodal image registration in biopsy puncture","authors":"Xudong Guo ,&nbsp;Peiyu Chen ,&nbsp;Haifeng Wang ,&nbsp;Zhichao Yan ,&nbsp;Qinfen Jiang ,&nbsp;Rongjiang Wang ,&nbsp;Ji Bin","doi":"10.1016/j.media.2026.103938","DOIUrl":"10.1016/j.media.2026.103938","url":null,"abstract":"<div><div>Accurate registration of preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) images is essential to enhance the precision of biopsy punctures and targeted ablation procedures using robotic systems. To improve the speed and accuracy of registration algorithms while accounting for soft tissue deformation during puncture, we propose UTMorph, a hybrid framework consisting of convolutional neural network (CNN) and Transformer network, based on the U-Net architecture. This model is designed to enable efficient and deformable multimodal image registration. We introduced a novel attention mechanism that focuses on the structured features of images, thereby ensuring precise deformation estimation and reducing computational complexity. In addition, we proposed a hybrid edge loss function to complement the shape and boundary information, thereby improving registration accuracy. Experiments were conducted on data from 704 patients, including private datasets from Shanghai East Hospital, public datasets from The Cancer Imaging Archive, and the µ-ProReg Challenge. The performance of UTMorph was compared with that of six commonly used registration methods and loss functions. UTMorph achieved superior performance across multiple evaluation metrics (dice similarity coefficient: 0.890, 95th percentile Hausdorff distance: 2.679 mm, mean surface distance: 0.284 mm, and Jacobi determinant: 0.040) and ensures accurate registration with minimal memory usage, even under significant modal differences. These findings validate the effectiveness of the UTMorph model with the hybrid edge loss function for MR-US deformable medical image registration. This code is available at <span><span>https://github.com/Prps7/UTMorph</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103938"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956952","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}