Medical image analysis最新文献

{"title":"DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation","authors":"Jiansong Fan ,&nbsp;Qi Sun ,&nbsp;Yicheng Di ,&nbsp;Jiayu Bao ,&nbsp;Tianxu Lv ,&nbsp;Yuan Liu ,&nbsp;Xiaoyun Hu ,&nbsp;Lihua Li ,&nbsp;Xiaobin Cui ,&nbsp;Xiang Pan","doi":"10.1016/j.media.2025.103563","DOIUrl":"10.1016/j.media.2025.103563","url":null,"abstract":"<div><div>Accurate segmentation of pathology images plays a crucial role in digital pathology workflow. However, two significant issues exist with the present pathology image segmentation methods: (i) Most fully supervised models rely on dense pixel-level annotations for superior results; (ii) Traditional static models are challenging to handle the massive amount of pathology data in multiple domains. To address these issues, we propose a Domain-Incremental Weakly Supervised State-space Model (DIPathMamba) that not only segments pathology images using image-level labels but also dynamically learns new domain knowledge and preserves the discriminability of previous domains. We first design a shared feature extractor based on the state space model, which employs an efficient hardware-aware design. Specifically, we extract pixel-level feature maps based on Multi-Instance Multi-Label Learning by treating pixels as instances, which are injected into our designed Contrastive Mamba Block (CMB). The CMB adopts a state space model and integrates the concept of contrastive learning to extract non-causal dual-granularity features in pathology images. Subsequently, to mitigate the performance degradation of prior domains during incremental learning, we design a Domain Parameter Constraint Model (DPCM). Finally, we propose a Collaborative Incremental Deep Supervision Loss (CIDSL), which aims to fully utilize the limited annotated information in weakly supervised methods and guide parameter learning during domain increment. Our approach integrates complex details and broader global contextual semantics in pathology images and can generate regionally more consistent segmentation results. Experiments on three public pathology image datasets show that the proposed method performs better than state-of-the-art methods.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103563"},"PeriodicalIF":10.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799490","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":"Unsupervised brain MRI tumour segmentation via two-stage image synthesis","authors":"Xinru Zhang ,&nbsp;Ni Ou ,&nbsp;Chenghao Liu ,&nbsp;Zhizheng Zhuo ,&nbsp;Paul M. Matthews ,&nbsp;Yaou Liu ,&nbsp;Chuyang Ye ,&nbsp;Wenjia Bai","doi":"10.1016/j.media.2025.103568","DOIUrl":"10.1016/j.media.2025.103568","url":null,"abstract":"<div><div>Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real and synthetic data limits the accuracy of the unsupervised approaches. In this paper, we propose an approach for unsupervised brain tumour segmentation on <em>magnetic resonance</em> (MR) images via a two-stage image synthesis strategy. This approach accounts for the domain gap between real and synthetic data and aims to generate realistic synthetic data for model training. In the first stage, we train a junior segmentation model using synthetic brain tumour images generated by hand-crafted tumour shape and intensity models, and employs a validation set with distribution shift for model selection. The trained junior model is applied to segment unlabelled real tumour images, generating pseudo labels that capture realistic tumour shape, intensity, and texture. In the second stage, realistic synthetic tumour images are generated by mixing brain images with tumour pseudo labels, closing the domain gap between real and synthetic images. The generated synthetic data is then used to train a senior model for final segmentation. In experiments on five brain imaging datasets, the proposed approach, named as <em>SynthTumour</em>, surpasses existing unsupervised methods and demonstrates high performance for both brain tumour segmentation and ischemic stroke lesion segmentation tasks.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103568"},"PeriodicalIF":10.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection","authors":"Fakrul Islam Tushar ,&nbsp;Liesbeth Vancoillie ,&nbsp;Cindy McCabe ,&nbsp;Amareswararao Kavuri ,&nbsp;Lavsen Dahal ,&nbsp;Brian Harrawood ,&nbsp;Milo Fryling ,&nbsp;Mojtaba Zarei ,&nbsp;Saman Sotoudeh-Paima ,&nbsp;Fong Chi Ho ,&nbsp;Dhrubajyoti Ghosh ,&nbsp;Michael R. Harowicz ,&nbsp;Tina D. Tailor ,&nbsp;Sheng Luo ,&nbsp;W. Paul Segars ,&nbsp;Ehsan Abadi ,&nbsp;Kyle J. Lafata ,&nbsp;Joseph Y. Lo ,&nbsp;Ehsan Samei","doi":"10.1016/j.media.2025.103576","DOIUrl":"10.1016/j.media.2025.103576","url":null,"abstract":"<div><div>Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an <em>in silico</em> study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95% CI: 0.90-0.95) compared to the AI CXR-Reader's AUC of 0.72 (95% CI: 0.67-0.77). Furthermore, at the same 94% CT sensitivity reported by the NLST, the VLST specificity of 73% was similar to the NLST specificity of 73.4%. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103576"},"PeriodicalIF":10.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808364","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":"ArtiDiffuser: A unified framework for artifact restoration and synthesis for histology images via counterfactual diffusion model","authors":"Chong Wang ,&nbsp;Kaili Qu ,&nbsp;Shuxin Li ,&nbsp;Yi Yu ,&nbsp;Junjun He ,&nbsp;Chen Zhang ,&nbsp;Yiqing Shen","doi":"10.1016/j.media.2025.103567","DOIUrl":"10.1016/j.media.2025.103567","url":null,"abstract":"<div><div>Artifacts in histology images pose challenges for accurate diagnosis with deep learning models, often leading to misinterpretations. Existing artifact restoration methods primarily rely on Generative Adversarial Networks (GANs), which approach the problem as image-to-image translation. However, those approaches are prone to mode collapse and can unexpectedly alter morphological features or staining styles. To address the issue, we propose <span>ArtiDiffuser</span>, a counterfactual diffusion model tailored to restore only artifact-distorted regions while preserving the integrity of the rest of the image. Additionally, we show an innovative perspective by addressing the misdiagnosis stemming from artifacts via artifact synthesis as data augmentation, and thereby leverage <span>ArtiDiffuser</span> to unify the artifact synthesis and the restoration capabilities. This synergy significantly surpasses the performance of conventional methods which separately handle artifact restoration or synthesis. We propose a Swin-Transformer denoising network backbone to capture both local and global attention, further enhanced with a class-guided Mixture of Experts (MoE) to process features related to specific artifact categories. Moreover, it utilizes adaptable class-specific tokens for enhanced feature discrimination and a mask-weighted loss function to specifically target and correct artifact-affected regions, thus addressing issues of data imbalance. In downstream applications, <span>ArtiDiffuser</span> employs a consistency regularization strategy that assures the model’s predictive accuracy is maintained across original and artifact-augmented images. We also contribute the first comprehensive histology dataset, comprising 723 annotated patches across various artifact categories, to facilitate further research. Evaluations on four distinct datasets for both restoration and synthesis demonstrate <span>ArtiDiffuser</span>’s effectiveness compared to GAN-based approaches, used for either pre-processing or augmentation. The code is available at <span><span>https://github.com/wagnchogn/ArtiDiffuser</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103567"},"PeriodicalIF":10.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783121","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":"MedScale-Former: Self-guided multiscale transformer for medical image segmentation","authors":"Sanaz Karimijafarbigloo ,&nbsp;Reza Azad ,&nbsp;Amirhossein Kazerouni ,&nbsp;Dorit Merhof","doi":"10.1016/j.media.2025.103554","DOIUrl":"10.1016/j.media.2025.103554","url":null,"abstract":"<div><div>Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive labeled training data. To address this limitation, our novel approach introduces a dual-branch transformer network operating on two scales, strategically encoding global contextual dependencies while preserving local information. To promote self-supervised learning, our method leverages semantic dependencies between different scales, generating a supervisory signal for inter-scale consistency. Additionally, it incorporates a spatial stability loss within each scale, fostering self-supervised content clustering. While intra-scale and inter-scale consistency losses enhance feature uniformity within clusters, we introduce a cross-entropy loss function atop the clustering score map to effectively model cluster distributions and refine decision boundaries. Furthermore, to account for pixel-level similarities between organ or lesion subpixels, we propose a selective kernel regional attention module as a plug and play component. This module adeptly captures and outlines organ or lesion regions, slightly enhancing the definition of object boundaries. Our experimental results on skin lesion, lung organ, and multiple myeloma plasma cell segmentation tasks demonstrate the superior performance of our method compared to state-of-the-art approaches.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103554"},"PeriodicalIF":10.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808365","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":"UAE: Universal Anatomical Embedding on multi-modality medical images","authors":"Xiaoyu Bai ,&nbsp;Fan Bai ,&nbsp;Xiaofei Huo ,&nbsp;Jia Ge ,&nbsp;Jingjing Lu ,&nbsp;Xianghua Ye ,&nbsp;Minglei Shu ,&nbsp;Ke Yan ,&nbsp;Yong Xia","doi":"10.1016/j.media.2025.103562","DOIUrl":"10.1016/j.media.2025.103562","url":null,"abstract":"<div><div>Identifying anatomical structures (<em>e.g.</em>, lesions or landmarks) is crucial for medical image analysis. Exemplar-based landmark detection methods are gaining attention as they allow the detection of arbitrary points during inference without needing annotated landmarks during training. These methods use self-supervised learning to create a discriminative voxel embedding and match corresponding landmarks via nearest-neighbor searches, showing promising results. However, current methods still face challenges in (1) differentiating voxels with similar appearance but different semantic meanings (<em>e.g.</em>, two adjacent structures without clear borders); (2) matching voxels with similar semantics but markedly different appearance (<em>e.g.</em>, the same vessel before and after contrast injection); and (3) cross-modality matching (<em>e.g.</em>, CT-MRI landmark-based registration). To overcome these challenges, we propose a Unified framework for learning Anatomical Embeddings (UAE). UAE is designed to learn appearance, semantic, and cross-modality anatomical embeddings. Specifically, UAE incorporates three key innovations: (1) semantic embedding learning with prototypical contrastive loss; (2) a fixed-point-based matching strategy; and (3) an iterative approach for cross-modality embedding learning. We thoroughly evaluated UAE across intra- and inter-modality tasks, including one-shot landmark detection, lesion tracking on longitudinal CT scans, and CT-MRI affine/rigid registration with varying fields of view. Our results suggest that UAE outperforms state-of-the-art methods, offering a robust and versatile approach for landmark-based medical image analysis tasks. Code and trained models are available at: <span><span>https://shorturl.at/bgsB3</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103562"},"PeriodicalIF":10.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799489","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":"Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography","authors":"Ju Hwan Lee ,&nbsp;Seong Je Oh ,&nbsp;Kyungsu Kim ,&nbsp;Chae Yeon Lim ,&nbsp;Seung Hong Choi ,&nbsp;Myung Jin Chung","doi":"10.1016/j.media.2025.103559","DOIUrl":"10.1016/j.media.2025.103559","url":null,"abstract":"<div><div>Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of utilizing local features, exhibits vulnerability in detecting deep lesions within the lungs. In other words, while the conventional use of global features can achieve high specificity, it often comes with limited sensitivity. Developing a UAD AI model with high sensitivity is essential to prevent false negatives, especially in screening patients with diseases demonstrating high mortality rates. We have successfully pioneered a new LDCT UAD AI model that leverages local features, achieving a previously unattainable increase in sensitivity compared to global methods (17.5% improvement). Furthermore, by integrating this approach with conventional global-based techniques, we have successfully consolidated the advantages of each model – high sensitivity from the local model and high specificity from the global model – into a single, unified, trained model (17.6% and 33.5% improvement, respectively). Without the need for additional training, we anticipate achieving significant diagnostic efficacy in various LDCT applications, where both high sensitivity and specificity are essential, using our fixed model. Code is available at <span><span>https://github.com/kskim-phd/Fusion-UADL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103559"},"PeriodicalIF":10.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785951","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":"M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis","authors":"Junyu Li ,&nbsp;Ye Zhang ,&nbsp;Wen Shu ,&nbsp;Xiaobing Feng ,&nbsp;Yingchun Wang ,&nbsp;Pengju Yan ,&nbsp;Xiaolin Li ,&nbsp;Chulin Sha ,&nbsp;Min He","doi":"10.1016/j.media.2025.103561","DOIUrl":"10.1016/j.media.2025.103561","url":null,"abstract":"<div><div>Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers. However, existing MIL methods predominantly focus on single-task learning, resulting in not only overall low efficiency but also the overlook of inter-task relatedness. To address these issues, we proposed an adapted architecture of Multi-gate Mixture-of-experts with Multi-proxy for Multiple instance learning (M4), and applied this framework for simultaneous prediction of multiple genetic mutations from WSIs. The proposed M4 model has two main innovations: (1) adopting a multi-gate mixture-of-experts strategy for multiple genetic mutation simultaneous prediction on a single WSI; (2) introducing a multi-proxy CNN construction on the expert and gate networks to effectively and efficiently capture patch-patch interactions from WSI. Our model achieved significant improvements across five tested TCGA datasets in comparison to current state-of-the-art single-task methods. The code is available at: <span><span>https://github.com/Bigyehahaha/M4</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"103 ","pages":"Article 103561"},"PeriodicalIF":10.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MeMGB-Diff: Memory-Efficient Multivariate Gaussian Bias Diffusion Model for 3D bias field correction","authors":"Xingyu Qiu ,&nbsp;Dong Liang ,&nbsp;Gongning Luo ,&nbsp;Xiangyu Li ,&nbsp;Wei Wang ,&nbsp;Kuanquan Wang ,&nbsp;Shuo Li","doi":"10.1016/j.media.2025.103560","DOIUrl":"10.1016/j.media.2025.103560","url":null,"abstract":"<div><div>Bias fields inevitably degrade MRI that seriously interferes the diagnosis of physicians for accurate analysis, and removing it is a crucial image analysis task. Generative models (such as GANs) are used for bias field correction, and outperform traditional methods, however are hindered by the high cost of data annotation and instability during training. Recently, the diffusion-based methods have excelled over GANs in many applications, and they are powerful in removing noise from images, while the bias field can be regarded as a smooth noise. However, it is a challenge to directly apply to 3D bias field correction due to sampling inefficiency, the heavy computational demand, and implicit correction process. We propose a Memory-Efficient Multivariate Gaussian Bias Diffusion Model (MeMGB-Diff) that is an explicit, sampling, and memory both efficient diffusion model for 3D bias field correction without using clinical labels. MeMGB-Diff extends the diffusion models to multivariate Gaussian and models the bias field as a multivariate Gaussian variable, allowing direct diffusion and removal of the 3D bias fields without Gaussian noise. For memory efficiency, MeMGB-Diff performs diffusion model in smaller readable image domain at the expense of a negligible accuracy loss, based on the strong correlation among adjacent voxels of bias field. We also propose a loss function to mainly learn the intensity trend, which mainly causes the inhomogeneity of MRI, and effectively increases the correction accuracy. For comprehensive performance comparison, we propose a synthetic method for generating more varied bias fields during testing. Both quantitative and qualitative assessments on synthetic and clinical data confirm the high fidelity and uniform intensity of our results. MeMGB-Diff <strong>reduces data size by 64 times</strong> to use less memory, <strong>improves sampling efficiency by more than 10 times</strong> compared to other diffusion-based methods, and achieves optimal metrics, including SSIM, PSNR, COCO, and CV for various tissues. Hence, our MeMGB-Diff is a state-of-the-art (SOTA) method for 3D bias field correction.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103560"},"PeriodicalIF":10.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guided ultrasound acquisition for nonrigid image registration using reinforcement learning","authors":"Shaheer U. Saeed ,&nbsp;João Ramalhinho ,&nbsp;Nina Montaña-Brown ,&nbsp;Ester Bonmati ,&nbsp;Stephen P. Pereira ,&nbsp;Brian Davidson ,&nbsp;Matthew J. Clarkson ,&nbsp;Yipeng Hu","doi":"10.1016/j.media.2025.103555","DOIUrl":"10.1016/j.media.2025.103555","url":null,"abstract":"<div><div>We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique interactive and spatially heterogeneous nature of this application to develop a registration algorithm that interactively suggests and acquires ultrasound images at optimised locations (with respect to registration performance). Our framework is based on two trainable functions: (1) a deep hyper-network-based registration function, which is generalisable over varying location and deformation, and adaptable at test-time; (2) a reinforcement learning function for producing test-time estimates of image acquisition locations and adapted deformation regularisation (the latter is required due to varying acquisition locations). We evaluate our proposed method with real preoperative patient data, and simulated intraoperative data with variable field-of-view. In addition to simulation of intraoperative data, we simulate global alignment based on previous work for efficient training, and investigate probe-level guidance towards an improved deformable registration. The evaluation in a simulated environment shows statistically significant improvements in overall registration performance across a variety of metrics for our proposed method, compared to registration without acquisition guidance or adaptable deformation regularisation, and to commonly used classical iterative methods and learning-based registration. For the first time, efficacy of proactive image acquisition is demonstrated in a simulated surgical interventional registration, in contrast to most existing work addressing registration post-data-acquisition, one of the reasons we argue may have led to previously under-constrained nonrigid registration in such applications. Code: <span><span>https://github.com/s-sd/rl_guided_registration</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103555"},"PeriodicalIF":10.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}