Medical image analysis最新文献

{"title":"A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis","authors":"Yi Zhang ,&nbsp;Yulin Yan ,&nbsp;Kun Wang ,&nbsp;Muyu Cai ,&nbsp;Yifei Xiang ,&nbsp;Yan Guo ,&nbsp;Puxun Tu ,&nbsp;Tao Ying ,&nbsp;Xiaojun Chen","doi":"10.1016/j.media.2026.103965","DOIUrl":"10.1016/j.media.2026.103965","url":null,"abstract":"<div><div>Handheld ultrasound (HHUS) is indispensable for breast cancer screening but remains compromised by operator-dependent acquisition, subjective 2D interpretation and clock-face annotation. Existing spatial tracking systems for HHUS typically lack integration, adaptability, flexibility, and robust 3D representation. Additionally, current deep learning diagnostic methods are predominantly based on single ultrasound images, whereas video-based malignancy classification approaches suffer from limited temporal interpretability. In this study, we develop an intelligent navigation-guided breast ultrasound scanning system delivering seamless 3D reconstruction, nipple-centric lesion localization, and video-based malignancy prediction with full adaptation to the routine workflow. Specifically, a Hybrid Lesion-informed Spatiotemporal Transformer (HLST) is proposed to selectively fuse intra- and peri-lesional dynamics augmented from a prompt-driven BUS-SAM-2 foundation model for sequence-level classification. Moreover, a geometry-adaptive clock projection and analysis method is designed to enable automated standardized clock-face orientation and lesion-to-nipple distance measurement for breasts of arbitrary shape, eliminating patient-attached fiducials or pre-marked landmarks. Validation on three breast phantoms demonstrated high correlations with CT reference (<em>r</em> &gt; 0.99 for distance, <em>r</em> &gt; 0.97 for 3D size, and <span><math><mrow><mi>r</mi><mo>=</mo><mn>1.00</mn></mrow></math></span> for clockwise angle, <em>p</em> &lt; 0.0001). Clinical evaluation in 43 female patients (30 abnormal breasts) yielded median clock-face orientation and size discrepancies of 0 h and 0.7 mm  ×  0.6 mm, respectively, versus conventional reports. Meanwhile, HLST achieved superior performance (86.1% accuracy) on the BUV dataset. By coupling precise 3D spatial annotation with foundation-model-enhanced spatiotemporal characterization, the proposed system offers a reliable, streamlined workflow that standardizes follow-up, guides biopsies, and promotes diagnostic confidence in HHUS practice.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103965"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072194","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":"DPFR: Semi-supervised gland segmentation via density perturbation and feature recalibration","authors":"Jiejiang Yu,&nbsp;Yu Liu","doi":"10.1016/j.media.2026.103962","DOIUrl":"10.1016/j.media.2026.103962","url":null,"abstract":"<div><div>In recent years, semi-supervised methods have attracted considerable attention in gland segmentation of histopathological images, as they can substantially reduce the annotation data burden for pathologists. The most widely adopted approach is the Mean-Teacher framework based on consistency regularization, which exploits unlabeled data information through consistency regularization constraints. However, due to the morphological complexity of glands in histopathological images, existing methods still suffer from confusion between glands and background, as well as gland adhesion. To address these challenges, we propose a semi-supervised gland segmentation method based on Density Perturbation and Feature Recalibration (DPFR). Specifically, we first design a normalized flow-based density estimator to effectively model the feature density distributions of glands, contours, and background. The gradient information of the estimator is then exploited to determine the descent direction in low-density regions, along which perturbations are applied to enhance feature discriminability. Furthermore, a contrastive-learning-based feature recalibration module is designed to alleviate inter-class distribution confusion, thereby improving gland-background separability and mitigating gland adhesion. Extensive experiments on three public gland segmentation datasets demonstrate that the proposed method consistently outperforms existing semi-supervised approaches, achieving state-of-the-art performance with a substantial margin. The code repository address is <span><span>https://github.com/Methow0/DPFR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103962"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072196","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 boundary confusion for volumetric medical image segmentation","authors":"Xin You ,&nbsp;Ming Ding ,&nbsp;Minghui Zhang ,&nbsp;Hanxiao Zhang ,&nbsp;Junyang Wu ,&nbsp;Yi Yu ,&nbsp;Jie Yang ,&nbsp;Yun Gu","doi":"10.1016/j.media.2026.103961","DOIUrl":"10.1016/j.media.2026.103961","url":null,"abstract":"<div><div>Accurate boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention. It is challenging to address the boundary confusion with explicit constraints. Existing methods of refining boundaries overemphasize the slender structure while overlooking the dynamic interactions between boundaries and neighboring regions. In this paper, we reconceptualize the mechanism of boundary generation via introducing Pushing and Pulling interactions, then propose a unified network termed PP-Net to model shape characteristics of the confused boundary region. Specifically, we first propose the semantic difference module (SDM) from the pushing branch to drive the boundary towards the ground truth under diffusion guidance. Additionally, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary along the opposite direction. Thus, pushing and pulling branches will furnish two adversarial forces to enhance representation capabilities for the faint boundary. Experiments are conducted on four public datasets and one in-house dataset plagued by boundary confusion. The results demonstrate the superiority of PP-Net over other segmentation networks, especially on the evaluation metrics of Hausdorff Distance and Average Symmetric Surface Distance. Besides, SDM and CCM can serve as plug-and-play modules to enhance classic U-shape baseline models, including recent SAM-based foundation models. Source codes are available at <span><span>https://github.com/EndoluminalSurgicalVision-IMR/PnPNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103961"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048365","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":"Fundus image quality assessment in retinopathy of prematurity via multi-label graph evidential network","authors":"Donghan Wu ,&nbsp;Wenyue Shen ,&nbsp;Lu Yuan ,&nbsp;Heng Li ,&nbsp;Huaying Hao ,&nbsp;Juan Ye ,&nbsp;Yitian Zhao","doi":"10.1016/j.media.2026.103959","DOIUrl":"10.1016/j.media.2026.103959","url":null,"abstract":"<div><div>Retinopathy of Prematurity (ROP) is a leading cause of childhood blindness worldwide. In clinical practice, fundus imaging serves as a primary diagnostic tool for ROP, making the accurate quality assessment of these images critically important. However, existing automated methods for evaluating ROP fundus images face significant challenges. First, there is a high degree of visual similarity between lesions and factors that influence quality. Second, there is a paucity of trustworthy outputs and interpretable or clinical-friendly designs, which limit their reliability and effectiveness. In this work, we propose a ROP image quality assessment framework, termed Q-ROP. This framework leverages fine-grained multi-label annotations based on key image factors such as artifacts, illumination, spatial positioning, and structural clarity. Additionally, the integration of a label graph network with evidential learning theory enables the model to explicitly capture the relationships between quality grades and influencing factors, thereby improving both robustness and accuracy. This approach facilitates interpretable analysis by directing the model’s focus toward relevant image features and reducing interference from lesion-like artifacts. Furthermore, the incorporation of evidential learning theory serves to quantify the uncertainty inherent in quality ratings, thereby ensuring the trustworthiness of the assessments. Trained and tested on a dataset of 6677 ROP images across three quality levels (i.e. acceptable, potentially acceptable, and unacceptable), Q-ROP achieved state-of-the-art performance with a 95.82% accuracy. Its effectiveness was further validated in a downstream ROP staging task, where it significantly improved the performance of typical classification models. These results demonstrate Q-ROP’s strong potential as a reliable and robust tool for clinical decision support.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103959"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048255","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":"Generative data-engine foundation model for universal few-shot 2D vascular image segmentation","authors":"Rongjun Ge ,&nbsp;Xin Li ,&nbsp;Yuxing Liu ,&nbsp;Chengliang Liu ,&nbsp;Pinzheng Zhang ,&nbsp;Jiong Zhang ,&nbsp;Jian Yang ,&nbsp;Jean-Louis Dillenseger ,&nbsp;Chunfeng Yang ,&nbsp;Yuting He ,&nbsp;Yang Chen","doi":"10.1016/j.media.2026.103996","DOIUrl":"10.1016/j.media.2026.103996","url":null,"abstract":"<div><div>The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread application. Developing a universal few-shot vascular segmentation model is highly desirable, yet remains challenging due to the need for extensive training and the inherent complexities of vascular imaging. In this work, we propose <strong>UniVG</strong> (Generative Data-engine Foundation Model for Universal Few-shot 2D Vascular Image Segmentation), a novel approach that learns the compositionality of vascular images and constructing a generative foundation model for robust vascular segmentation. UniVG enables the synthesis and learning of diverse and realistic vascular images through two key innovations: <em>1) Compositional learning</em> for flexible and diverse vascular synthesis: It decomposes and recombines vascular structures with varying morphological features and diverse foreground-background configurations to generate richly diverse synthetic image-label pairs. <em>2) Few-shot generative adaptation</em> for transferable segmentation: It fine-tunes pre-trained models with minimal annotated data to bridge the gap between synthetic and real vascular domains, synthesizing authentic and diverse vessel images for downstream few-shot vascular segmentation learning. To support our approach, we develop UniVG-58K, a large dataset comprising 58,689 vascular images across five imaging modalities, facilitating robust large-scale generative pre-training. Extensive experiments on 11 vessel segmentation tasks cross 5 modalties (only with 5 labeled images on each task) demonstrate that UniVG achieves performance comparable to fully supervised models, significantly reducing data collection and annotation costs. All code and datasets will be made publicly available at <span><span>https://github.com/XinAloha/UniVG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103996"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146209648","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":"BCIRT: Backscattering-corrected implicit representation tomography","authors":"Chuanhao Zhang ,&nbsp;Yangxi Li ,&nbsp;Jianping Song ,&nbsp;Yingwei Fan ,&nbsp;Guochen Ning ,&nbsp;Yu Shen ,&nbsp;Canhong Xiang ,&nbsp;Fang Chen ,&nbsp;Hongen Liao","doi":"10.1016/j.media.2026.104000","DOIUrl":"10.1016/j.media.2026.104000","url":null,"abstract":"<div><div>Optical coherence tomography (OCT) A-scan backscattering signals provide depth-resolved textural information about internal structures. However, conventional OCT imaging is limited by refraction-induced distortion and speckle noise, hindering fine detail resolution. While multi-angle imaging systems alleviate these issues through incoherent compounding of backscattering signals, in vivo applications face challenges: limited angular coverage during surface scanning degrades backscatter intensity compounding quality, and the absence of angular information introduces artifacts in multi-view position-intensity alignment. Furthermore, excessive smoothing during speckle suppression obscures fine textures. Consequently, reconstructing ultra-fine structures from limited-angle, sparse-view measurements remains a critical challenge. To address this, we present Backscattering-Corrected Implicit Representation Tomography (BCIRT), a framework for reconstructing multi-angle low-coherence signals. We also develop a dedicated limited-angle imaging system for intraoperative BCIRT deployment. BCIRT formulates cross-view backscattering signals as a continuous function of spatial position, utilizing implicit neural representation (INR) for fitting. A physics-informed iterative mechanism inversely models ray propagation to determine corrected ray paths, enhancing the neural representation’s robustness against distortions. Leveraging these corrected paths, we introduce a dual dynamic line mixer and a contrastive-guided discriminative deblurring module to achieve high-resolution microstructure reconstruction with reduced speckle noise. Extensive experiments on biological samples and surgical resected samples demonstrate that our method achieves state-of-the-art performance, highlighting its potential for clinical applications and biomedical research.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 104000"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777316","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":"Enhancing feature fusion of U-like networks with dynamic skip connections","authors":"Yue Cao,&nbsp;Quansong He,&nbsp;Kaishen Wang,&nbsp;Jianlong Xiong,&nbsp;Zhang Yi,&nbsp;Tao He","doi":"10.1016/j.media.2026.104010","DOIUrl":"10.1016/j.media.2026.104010","url":null,"abstract":"<div><div>U-like networks have become fundamental frameworks in medical image segmentation through skip connections that bridge high-level semantics and low-level spatial details. Despite their success, conventional skip connections exhibit two key limitations: inter-feature constraints and intra-feature constraints. The inter-feature constraint refers to the static nature of feature fusion in traditional skip connections, where information is transmitted along fixed pathways regardless of feature content. The intra-feature constraint arises from the insufficient modeling of multi-scale feature interactions, thereby hindering the effective aggregation of global contextual information. To overcome these limitations, we propose a novel Dynamic Skip Connection (DSC) block that fundamentally enhances cross-layer connectivity through adaptive mechanisms. The DSC block integrates two complementary components: (1) Test-Time Training (TTT) module: This module addresses the inter-feature constraint by enabling dynamic adaptation of hidden representations during inference, facilitating content-aware feature refinement. (2) Dynamic Multi-Scale Kernel (DMSK) module: To mitigate the intra-feature constraint, this module adaptively selects kernel sizes based on global contextual cues, enhancing the network’s capacity for multi-scale feature integration. The DSC block is architecture-agnostic and can be seamlessly incorporated into existing U-like network structures. Extensive experiments demonstrate the plug-and-play effectiveness of the proposed DSC block across CNN-based, Transformer-based, hybrid CNN-Transformer, and Mamba-based U-like networks. The code is available at <span><span>https://github.com/BlackJack-Cao/U-like-Networks-with-DSC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 104010"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147334541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomy-guided prompting with cross-modal self-alignment for whole-body PET-CT breast cancer segmentation","authors":"Jiaju Huang ,&nbsp;Xiao Yang ,&nbsp;Xinglong Liang ,&nbsp;Shaobin Chen ,&nbsp;Yue Sun ,&nbsp;Greta Sp Mok ,&nbsp;Shuo Li ,&nbsp;Ying Wang ,&nbsp;Tao Tan","doi":"10.1016/j.media.2026.103956","DOIUrl":"10.1016/j.media.2026.103956","url":null,"abstract":"<div><div>Accurate segmentation of breast cancer in PET-CT images is crucial for precise staging, monitoring treatment response, and guiding personalized therapy. However, the small size and dispersed nature of metastatic lesions, coupled with the scarcity of annotated data and heterogeneity between modalities that hinders effective information fusion, make this task challenging. This paper proposes a novel anatomy-guided cross-modal learning framework to address these issues. Our approach first generates organ pseudo-labels through a teacher-student learning paradigm, which serve as anatomical prompts to guide cancer segmentation. We then introduce a self-aligning cross-modal pre-training method that aligns PET and CT features in a shared latent space through masked 3D patch reconstruction, enabling effective cross-modal feature fusion. Finally, we initialize the segmentation network’s encoder with the pre-trained encoder weights, and incorporate organ labels through a Mamba-based prompt encoder and Hypernet-Controlled Cross-Attention mechanism for dynamic anatomical feature extraction and fusion. Notably, our method outperforms eight state-of-the-art methods, including CNN-based, transformer-based, and Mamba-based approaches, on two datasets encompassing primary breast cancer, metastatic breast cancer, and other types of cancer segmentation tasks.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103956"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033893","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":"Knowledge-guided multi-geometric window transformer for cardiac cine MRI reconstruction","authors":"Jun Lyu ,&nbsp;Guangming Wang ,&nbsp;Yunqi Wang ,&nbsp;Jing Qin ,&nbsp;Chengyan Wang","doi":"10.1016/j.media.2026.103936","DOIUrl":"10.1016/j.media.2026.103936","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) plays a crucial role in clinical diagnosis, yet traditional MR image acquisition often requires a prolonged duration, potentially causing patient discomfort and image artifacts. Faster and more accurate image reconstruction may alleviate patient discomfort during MRI examinations and enhance diagnostic accuracy and efficiency. In recent years, significant advancements in deep learning technology offer promise for improving MR image quality and accelerating acquisition. Addressing the demand for cardiac cine MRI reconstruction, we propose KGMgT, a novel MRI reconstruction network based on knowledge-guided approaches. The KGMgT model leverages adaptive spatiotemporal attention mechanisms to infer motion trajectories of adjacent cardiac frames, thereby better extracting complementary information. Additionally, we employ Transformer-driven dynamic feature aggregation to establish long-range dependencies, facilitating global information integration. Research findings demonstrate that the KGMgT model achieves state-of-the-art performance on multiple benchmark datasets, offering an efficient solution for cardiac cine MRI reconstruction. This collaborative approach, combining artificial intelligence technology to assist medical professionals in clinical decision-making, holds promise for significantly improving diagnostic efficiency, optimizing treatment plans, and enhancing the patient treatment experience. The code and trained models are available at <span><span>https://github.com/MICV-Lab/KGMgT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103936"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956958","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":"CATERPillar: a flexible framework for generating white matter numerical substrates with incorporated glial cells","authors":"Jasmine Nguyen-Duc ,&nbsp;Malte Brammerloh ,&nbsp;Melina Cherchali ,&nbsp;Inès De Riedmatten ,&nbsp;Jean-Baptiste Pérot ,&nbsp;Jonathan Rafael-Patiño ,&nbsp;Ileana O. Jelescu","doi":"10.1016/j.media.2026.103946","DOIUrl":"10.1016/j.media.2026.103946","url":null,"abstract":"<div><div>Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of numerical phantoms that accurately represent the target tissue, which is in this case, cerebral white matter (WM). This study introduces CATERPillar (Computational Axonal Threading Engine for Realistic Proliferation), a novel method that simulates the mechanics of axonal growth using overlapping spheres as elementary units. CATERPillar facilitates parallel axon development while preventing collisions, offering user control over key structural parameters such as cellular density, undulation, beading and myelination. Its uniqueness lies in its ability to generate not only realistic axonal structures but also realistic glial cells, enhancing the biological fidelity of simulations. We showed that our grown substrates feature distributions of key morphological parameters that agree with those from histological studies. The structural realism of the astrocytic components was quantitatively validated using Sholl analysis. Furthermore, the time-dependent diffusion in the extra- and intra-axonal compartments accurately reflected expected characteristics of short-range disorder, as predicted by theoretical models. CATERPillar is open source and can be used to (a) develop new acquisition schemes that sensitise the MRI signal to unique tissue microstructure features, (b) test the accuracy of a broad range of analytical models, and (c) build a set of substrates to train machine learning models on.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"110 ","pages":"Article 103946"},"PeriodicalIF":11.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995244","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}