Medical image analysis最新文献

{"title":"MBSS-T1: Model-based subject-specific self-supervised motion correction for robust cardiac T1 mapping","authors":"Eyal Hanania ,&nbsp;Adi Zehavi-Lenz ,&nbsp;Ilya Volovik ,&nbsp;Daphna Link-Sourani ,&nbsp;Israel Cohen ,&nbsp;Moti Freiman","doi":"10.1016/j.media.2025.103495","DOIUrl":"10.1016/j.media.2025.103495","url":null,"abstract":"<div><div>Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSS-T1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. In a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>: 0.975 vs. 0.941, 0.946 for STONE; 0.987 vs. 0.982, 0.965 for MOLLI free-breathing; 0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE; 0.963 vs. 0.919, 0.851 for MOLLI free-breathing; 0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE; 4.1 vs. 3.5, 3.28 for MOLLI free-breathing; 3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at <span><span>https://github.com/TechnionComputationalMRILab/MBSS-T1</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103495"},"PeriodicalIF":10.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471189","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":"Neighbor-aware calibration of segmentation networks with penalty-based constraints","authors":"Balamurali Murugesan ,&nbsp;Sukesh Adiga Vasudeva ,&nbsp;Bingyuan Liu ,&nbsp;Herve Lombaert ,&nbsp;Ismail Ben Ayed ,&nbsp;Jose Dolz","doi":"10.1016/j.media.2025.103501","DOIUrl":"10.1016/j.media.2025.103501","url":null,"abstract":"<div><div>Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has resulted in substantial progress. Nevertheless, these approaches are strongly inspired by the advancements in classification tasks, and thus their uncertainty is usually modeled by leveraging the information of individual pixels, disregarding the local structure of the object of interest. Indeed, only the recent <em>Spatially Varying Label Smoothing (SVLS)</em> approach considers pixel spatial relationships across classes, by softening the pixel label assignments with a discrete spatial Gaussian kernel. In this work, we first present a constrained optimization perspective of SVLS and demonstrate that it enforces an implicit constraint on soft class proportions of surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism to balance the contribution of the constraint with the primary objective, potentially hindering the optimization process. Based on these observations, we propose NACL (Neighbor Aware CaLibration), a principled and simple solution based on equality constraints on the logit values, which enables to control explicitly both the enforced constraint and the weight of the penalty, offering more flexibility. Comprehensive experiments on a wide variety of well-known segmentation benchmarks demonstrate the superior calibration performance of the proposed approach, without affecting its discriminative power. Furthermore, ablation studies empirically show the model agnostic nature of our approach, which can be used to train a wide span of deep segmentation networks. The code is available at <span><span>https://github.com/Bala93/MarginLoss</span><svg><path></path></svg></span></div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103501"},"PeriodicalIF":10.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436448","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":"A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation","authors":"Ziyan Huang ,&nbsp;Zhongying Deng ,&nbsp;Jin Ye ,&nbsp;Haoyu Wang ,&nbsp;Yanzhou Su ,&nbsp;Tianbin Li ,&nbsp;Hui Sun ,&nbsp;Junlong Cheng ,&nbsp;Jianpin Chen ,&nbsp;Junjun He ,&nbsp;Yun Gu ,&nbsp;Shaoting Zhang ,&nbsp;Lixu Gu ,&nbsp;Yu Qiao","doi":"10.1016/j.media.2025.103499","DOIUrl":"10.1016/j.media.2025.103499","url":null,"abstract":"<div><div>Although deep learning has revolutionized abdominal multi-organ segmentation, its models often struggle with generalization due to training on small-scale, specific datasets and modalities. The recent emergence of large-scale datasets may mitigate this issue, but some important questions remain unsolved: <strong>Can models trained on these large datasets generalize well across different datasets and imaging modalities? If yes/no, how can we further improve their generalizability?</strong> To address these questions, we introduce A-Eval, a benchmark for the cross-dataset and cross-modality Evaluation (’Eval’) of Abdominal (’A’) multi-organ segmentation, integrating seven datasets across CT and MRI modalities. Our evaluations indicate that significant domain gaps persist despite larger data scales. While increased datasets improve generalization, model performance on unseen data remains inconsistent. Joint training across multiple datasets and modalities enhances generalization, though annotation inconsistencies pose challenges. These findings highlight the need for diverse and well-curated training data across various clinical scenarios and modalities to develop robust medical imaging models. The code and pre-trained models are available at <span><span>https://github.com/uni-medical/A-Eval</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103499"},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436445","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":"From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare","authors":"Ming Li ,&nbsp;Pengcheng Xu ,&nbsp;Junjie Hu ,&nbsp;Zeyu Tang ,&nbsp;Guang Yang","doi":"10.1016/j.media.2025.103497","DOIUrl":"10.1016/j.media.2025.103497","url":null,"abstract":"<div><div>Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103497"},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418562","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":"A survey of intracranial aneurysm detection and segmentation","authors":"Wei-Chan Hsu ,&nbsp;Monique Meuschke ,&nbsp;Alejandro F. Frangi ,&nbsp;Bernhard Preim ,&nbsp;Kai Lawonn","doi":"10.1016/j.media.2025.103493","DOIUrl":"10.1016/j.media.2025.103493","url":null,"abstract":"<div><div>Intracranial aneurysms (IAs) are a critical public health concern: they are asymptomatic and can lead to fatal subarachnoid hemorrhage in case of rupture. Neuroradiologists rely on advanced imaging techniques to identify aneurysms in a patient and consider the characteristics of IAs along with several other patient-related factors for rupture risk assessment and treatment decision-making. The process of diagnostic image reading is time-intensive and prone to inter- and intra-individual variations, so researchers have proposed many computer-aided diagnosis (CAD) systems for aneurysm detection and segmentation. This paper provides a comprehensive literature survey of semi-automated and automated approaches for IA detection and segmentation and proposes a taxonomy to classify the approaches. We also discuss the current issues and give some insight into the future direction of CAD systems for IA detection and segmentation.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103493"},"PeriodicalIF":10.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436444","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":"Learning robust medical image segmentation from multi-source annotations","authors":"Yifeng Wang ,&nbsp;Luyang Luo ,&nbsp;Mingxiang Wu ,&nbsp;Qiong Wang ,&nbsp;Hao Chen","doi":"10.1016/j.media.2025.103489","DOIUrl":"10.1016/j.media.2025.103489","url":null,"abstract":"<div><div>Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. However, learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of the annotations. In this paper, we proposed an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guided the training process by uncertainty estimation at both the pixel and the image levels. First, we developed an annotation uncertainty estimation module (AUEM) to estimate the pixel-wise uncertainty of each annotation, which then guided the network to learn from reliable pixels by a weighted segmentation loss. Second, a quality assessment module (QAM) was proposed to assess the image-level quality of the input samples based on the former estimated annotation uncertainties. Furthermore, instead of discarding the low-quality samples, we introduced an auxiliary predictor to learn from them and thus ensured the preservation of their representation knowledge in the backbone without directly accumulating errors within the primary predictor. Extensive experiments demonstrated the effectiveness and feasibility of our proposed UMA-Net on various datasets, including 2D chest X-ray segmentation dataset, 2D fundus image segmentation dataset, 3D breast DCE-MRI segmentation dataset, and the QUBIQ multi-task segmentation dataset. Code will be released at <span><span>https://github.com/wangjin2945/UMA-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103489"},"PeriodicalIF":10.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376589","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":"HistoKernel: Whole slide image level Maximum Mean Discrepancy kernels for pan-cancer predictive modelling","authors":"Piotr Keller ,&nbsp;Muhammad Dawood ,&nbsp;Brinder Singh Chohan ,&nbsp;Fayyaz ul Amir Afsar Minhas","doi":"10.1016/j.media.2025.103491","DOIUrl":"10.1016/j.media.2025.103491","url":null,"abstract":"<div><div>In computational pathology, labels are typically available only at the whole slide image (WSI) or patient level, necessitating weakly supervised learning methods that aggregate patch-level features or predictions to produce WSI-level scores for clinically significant tasks such as cancer subtype classification or survival analysis. However, existing approaches lack a theoretically grounded framework to capture the holistic distributional differences between the patch sets within WSIs, limiting their ability to accurately and comprehensively model the underlying pathology. To address this limitation, we introduce HistoKernel, a novel WSI-level Maximum Mean Discrepancy (MMD) kernel designed to quantify distributional similarity between WSIs using their local feature representation. HistoKernel enables a wide range of applications, including classification, regression, retrieval, clustering, survival analysis, multimodal data integration, and visualization of large WSI datasets. Additionally, HistoKernel offers a novel perturbation-based method for patch-level explainability. Our analysis over large pan-cancer datasets shows that HistoKernel achieves performance that typically matches or exceeds existing state-of-the-art methods across diverse tasks, including WSI retrieval (n = 9324), drug sensitivity regression (n = 551), point mutation classification (n = 3419), and survival analysis (n = 2291). By pioneering the use of kernel-based methods for a diverse range of WSI-level predictive tasks, HistoKernel opens new avenues for computational pathology research especially in terms of rapid prototyping on large and complex computational pathology datasets. Code and interactive visualization are available at: <span><span>https://histokernel.dcs.warwick.ac.uk/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103491"},"PeriodicalIF":10.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387188","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":"Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical parcellation","authors":"Yuanzhuo Zhu ,&nbsp;Xianjun Li ,&nbsp;Chen Niu ,&nbsp;Fan Wang ,&nbsp;Jianhua Ma","doi":"10.1016/j.media.2025.103492","DOIUrl":"10.1016/j.media.2025.103492","url":null,"abstract":"<div><div>Cortical parcellation delineates the cerebral cortex into distinct regions according to their distinctiveness in anatomy and/or function, which is a fundamental preprocess in brain cortex analysis and can influence the accuracy and specificity of subsequent neuroscientific research and clinical diagnosis. Conventional methods for cortical parcellation involve spherical mapping and multiple morphological feature computation, which are time-consuming and prone to error due to the spherical mapping process. Recent geometric learning approaches have attempted to automate this process by replacing the registration-based parcellation with deep learning-based methods. However, they have not fully addressed spherical mapping and cortical features quantification, making them sensitive to variations in mesh structures. In this work, to directly parcellate original surfaces in individual space with minimal preprocessing, we present a full-band spectral-accelerated spatial diffusion strategy for stable information propagation on highly folded cortical surfaces, contributing to adaptive learning of fine-grained geometric representations and the construction of a compact deep network (termed Cortex-Diffusion) for fully automatic parcellation. Using only raw 3D vertex coordinates and having merely 0.49 MB of learnable parameters, it demonstrates state-of-the-art parcellation accuracy, efficiency, and superior robustness to mesh resolutions and discretization patterns in both the cases of infant and adult brain imaging datasets.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103492"},"PeriodicalIF":10.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418559","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":"Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks","authors":"Jingjing Gao ,&nbsp;Mingqi Liu ,&nbsp;Maomin Qian ,&nbsp;Heping Tang ,&nbsp;Junyi Wang ,&nbsp;Liang Ma ,&nbsp;Yanling Li ,&nbsp;Xin Dai ,&nbsp;Zhengning Wang ,&nbsp;Fengmei Lu ,&nbsp;Fan Zhang","doi":"10.1016/j.media.2025.103482","DOIUrl":"10.1016/j.media.2025.103482","url":null,"abstract":"<div><div>The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity for precise striatal segmentation. In this study, we introduce a novel deep clustering pipeline for automated, fine-scale parcellation of the striatum using diffusion MRI (dMRI) tractography. Initially, we employ a voxel-based probabilistic fiber tractography algorithm combined with a fiber-tract embedding technique to capture intricate dMRI connectivity patterns. To maintain critical inter-voxel relationships, our approach employs Graph Neural Networks (GNNs) to create accurate graph representations of the striatum. This involves encoding probabilistic fiber bundle characteristics as node attributes and refining edge weights using activation functions to enhance the graph’s interpretability and accuracy. The methodology incorporates a Transformer-based GraphConv autoencoder in the pre-training phase to extract critical spatial features while minimizing reconstruction loss. In the fine-tuning phase, a novel joint loss mechanism markedly improves segmentation precision and anatomical fidelity. Integration of traditional clustering techniques with multi-head self-attention mechanisms further elevates the accuracy and robustness of our segmentation approach. This methodology provides new insights into the striatum’s role in cognition and behavior and offers potential clinical applications for neurological disorders.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103482"},"PeriodicalIF":10.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction","authors":"Peng Li,&nbsp;Yue Hu","doi":"10.1016/j.media.2025.103481","DOIUrl":"10.1016/j.media.2025.103481","url":null,"abstract":"<div><div>Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed images. Existing model-based methods can mitigate aliasing artifacts and enhance reconstruction quality but suffer from long reconstruction times. In addition, data priors used in these methods, such as low-rank and total variation, make it challenging to incorporate non-local and non-linear redundancies in MRF data. Furthermore, existing deep learning-based methods for MRF often lack interpretability and struggle with the high computational overhead caused by the high dimensionality of MRF data. To address these issues, we introduce a novel deep graph embedding framework based on the Laplacian eigenmaps for improved MRF reconstruction. Our work first models the acquired high-dimensional MRF data and the corresponding parameter maps as graph data nodes. Then, we propose an MRF reconstruction framework based on the graph embedding framework, retaining intrinsic graph structures between parameter maps and MRF data. To improve the accuracy of the estimated graph structure and the computational efficiency of the proposed framework, we unroll the iterative optimization process into a deep neural network, incorporating a learned graph embedding module to adaptively learn the Laplacian eigenmaps. By introducing the graph embedding framework into the MRF reconstruction, the proposed method can effectively exploit non-local and non-linear correlations in MRF data. Numerical experiments demonstrate that our approach can reconstruct high-quality MRF data and multiple parameter maps within a significantly reduced computational cost.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103481"},"PeriodicalIF":10.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350431","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}