Medical image analysis最新文献

{"title":"Joint modeling histology and molecular markers for cancer classification","authors":"Xiaofei Wang ,&nbsp;Hanyu Liu ,&nbsp;Yupei Zhang ,&nbsp;Boyang Zhao ,&nbsp;Hao Duan ,&nbsp;Wanming Hu ,&nbsp;Yonggao Mou ,&nbsp;Stephen Price ,&nbsp;Chao Li","doi":"10.1016/j.media.2025.103505","DOIUrl":"10.1016/j.media.2025.103505","url":null,"abstract":"<div><div>Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Although digital pathology has been advancing cancer diagnosis and prognosis, the paradigm in cancer pathology has shifted from purely relying on histology features to incorporating molecular markers. There is an urgent need for digital pathology methods to meet the needs of the new paradigm. We introduce a novel digital pathology approach to jointly predict molecular markers and histology features and model their interactions for cancer classification. Firstly, to mitigate the challenge of cross-magnification information propagation, we propose a multi-scale disentangling module, enabling the extraction of multi-scale features from high-magnification (cellular-level) to low-magnification (tissue-level) whole slide images. Further, based on the multi-scale features, we propose an attention-based hierarchical multi-task multi-instance learning framework to simultaneously predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correlation graph network to model the co-occurrence of molecular markers. Lastly, we design a cross-modal interaction module with the dynamic confidence constrain loss and a cross-modal gradient modulation strategy, to model the interactions of histology and molecular markers. Our experiments demonstrate that our method outperforms other state-of-the-art methods in classifying glioma, histology features and molecular markers. Our method promises to promote precise oncology with the potential to advance biomedical research and clinical applications. The code is available at <span><span>github</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103505"},"PeriodicalIF":10.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478696","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":"CVFSNet: A Cross View Fusion Scoring Network for end-to-end mTICI scoring","authors":"Weijin Xu ,&nbsp;Tao Tan ,&nbsp;Huihua Yang ,&nbsp;Wentao Liu ,&nbsp;Yifu Chen ,&nbsp;Ling Zhang ,&nbsp;Xipeng Pan ,&nbsp;Feng Gao ,&nbsp;Yiming Deng ,&nbsp;Theo van Walsum ,&nbsp;Matthijs van der Sluijs ,&nbsp;Ruisheng Su","doi":"10.1016/j.media.2025.103508","DOIUrl":"10.1016/j.media.2025.103508","url":null,"abstract":"<div><div>The modified Thrombolysis In Cerebral Infarction (mTICI) score serves as one of the key clinical indicators to assess the success of the Mechanical Thrombectomy (MT), requiring physicians to inspect Digital Subtraction Angiography (DSA) images in both the coronal and sagittal views. However, assessing mTICI scores manually is time-consuming and has considerable observer variability. An automatic, objective, and end-to-end method for assigning mTICI scores may effectively avoid observer errors. Therefore, in this paper, we propose a novel Cross View Fusion Scoring Network (CVFSNet) for automatic, objective, and end-to-end mTICI scoring, which employs dual branches to simultaneously extract spatial–temporal features from coronal and sagittal views. Then, a novel Cross View Fusion Module (CVFM) is introduced to fuse the features from two views, which explores the positional characteristics of coronal and sagittal views to generate a pseudo-oblique sagittal feature and ultimately constructs more representative features to enhance the scoring performance. In addition, we provide AmTICIS, a newly collected and the first publicly available DSA image dataset with expert annotations for automatic mTICI scoring, which can effectively promote researchers to conduct studies of ischemic stroke based on DSA images and finally help patients get better medical treatment. Extensive experimentation results demonstrate the promising performance of our methods and the validity of the cross-view fusion module. Code and data will be available at <span><span>https://github.com/xwjBupt/CVFSNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103508"},"PeriodicalIF":10.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508156","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":"MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging","authors":"Yuanye Liu ,&nbsp;Zheyao Gao ,&nbsp;Nannan Shi ,&nbsp;Fuping Wu ,&nbsp;Yuxin Shi ,&nbsp;Qingchao Chen ,&nbsp;Xiahai Zhuang","doi":"10.1016/j.media.2025.103507","DOIUrl":"10.1016/j.media.2025.103507","url":null,"abstract":"<div><div>Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches simultaneously. However, previous multi-view approaches could not typically calculate uncertainty by nature, and they generally integrate features from different views in a black-box fashion, hence compromising reliability as well as interpretability of the resulting models. In this work, we propose a new multi-view method based on evidential learning, referred to as MERIT, which tackles the two challenges in a unified framework. MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability. Specifically, MERIT models the prediction from each sub-view as an opinion with quantified uncertainty under the guidance of the subjective logic theory. Furthermore, a distribution-aware base rate is introduced to enhance performance, particularly in scenarios involving class distribution shifts. Finally, MERIT adopts a feature-specific combination rule to explicitly fuse multi-view predictions, thereby enhancing interpretability. Results have showcased the effectiveness of the proposed MERIT, highlighting the reliability and offering both ad-hoc and post-hoc interpretability. They also illustrate that MERIT can elucidate the significance of each view in the decision-making process for liver fibrosis staging. Our code will be released via <span><span>https://github.com/HenryLau7/MERIT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103507"},"PeriodicalIF":10.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521224","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":"Large-scale benchmarking and boosting transfer learning for medical image analysis","authors":"Mohammad Reza Hosseinzadeh Taher ,&nbsp;Fatemeh Haghighi ,&nbsp;Michael B. Gotway ,&nbsp;Jianming Liang","doi":"10.1016/j.media.2025.103487","DOIUrl":"10.1016/j.media.2025.103487","url":null,"abstract":"<div><div>Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using different strategies. But, there is a lack of up-to-date large-scale evaluations of their transferability to medical imaging, posing a challenge for practitioners in selecting the most proper pretrained models for their tasks at hand. To fill this gap, we conduct a comprehensive systematic study, focusing on (<em>i</em>) benchmarking numerous conventional and modern convolutional neural network (ConvNet) and vision transformer architectures across various medical tasks; (<em>ii</em>) investigating the impact of fine-tuning data size on the performance of ConvNets compared with vision transformers in medical imaging; (<em>iii</em>) examining the impact of pretraining data granularity on transfer learning performance; (<em>iv</em>) evaluating transferability of a wide range of recent self-supervised methods with diverse training objectives to a variety of medical tasks across different modalities; and (<em>v</em>) delving into the efficacy of domain-adaptive pretraining on both photographic and medical datasets to develop high-performance models for medical tasks. Our large-scale study (<span><math><mo>∼</mo></math></span>5,000 experiments) yields impactful insights: (1) ConvNets demonstrate higher transferability than vision transformers when fine-tuning for medical tasks; (2) ConvNets prove to be more annotation efficient than vision transformers when fine-tuning for medical tasks; (3) Fine-grained representations, rather than high-level semantic features, prove pivotal for fine-grained medical tasks; (4) Self-supervised models excel in learning holistic features compared with supervised models; and (5) Domain-adaptive pretraining leads to performant models via harnessing knowledge acquired from ImageNet and enhancing it through the utilization of readily accessible expert annotations associated with medical datasets. As open science, all codes and pretrained models are available at <span><span>GitHub.com/JLiangLab/BenchmarkTransferLearning</span><svg><path></path></svg></span> (Version 2).</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103487"},"PeriodicalIF":10.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674377","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":"Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling","authors":"Daniel Duenias ,&nbsp;Brennan Nichyporuk ,&nbsp;Tal Arbel ,&nbsp;Tammy Riklin Raviv ,&nbsp;ADNI","doi":"10.1016/j.media.2025.103503","DOIUrl":"10.1016/j.media.2025.103503","url":null,"abstract":"<div><div>The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients’ Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a comprehensive understanding of the clinical condition of a patient, improving diagnosis and treatment decision. Deep Neural Networks (DNNs) consistently demonstrate outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR’s values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject’s sex and multi-class Alzheimer’s Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI tabular data fusion methods. A link to our code can be found at <span><span>https://github.com/daniel4725/HyperFusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103503"},"PeriodicalIF":10.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534140","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":"Preoperative fracture reduction planning for image-guided pelvic trauma surgery: A comprehensive pipeline with learning","authors":"Yanzhen Liu ,&nbsp;Sutuke Yibulayimu ,&nbsp;Yudi Sang ,&nbsp;Gang Zhu ,&nbsp;Chao Shi ,&nbsp;Chendi Liang ,&nbsp;Qiyong Cao ,&nbsp;Chunpeng Zhao ,&nbsp;Xinbao Wu ,&nbsp;Yu Wang","doi":"10.1016/j.media.2025.103506","DOIUrl":"10.1016/j.media.2025.103506","url":null,"abstract":"<div><div>Pelvic fractures are among the most complex challenges in orthopedic trauma, which usually involve hipbone and sacrum fractures, as well as joint dislocations. Traditional preoperative surgical planning relies on the operator’s subjective interpretation of CT images, which is both time-consuming and prone to inaccuracies. This study introduces an automated preoperative planning solution for pelvic fracture reduction, addressing the limitations of conventional methods. The proposed solution includes a novel multi-scale distance-weighted neural network for segmenting pelvic fracture fragments from CT scans, and a learning-based approach to restore pelvic structure, combining a morphable model-based method for single-bone fracture reduction and a recursive pose estimation module for joint dislocation reduction. Comprehensive experiments on a clinical dataset of 30 fracture cases demonstrated the efficacy of our methods. Our segmentation network outperformed traditional max-flow segmentation and networks without distance weighting, achieving a Dice similarity coefficient (DSC) of 0.986 ± 0.055 and a local DSC of 0.940 ± 0.056 around the fracture sites. The proposed reduction method surpassed mirroring and mean template techniques, and an optimization-based joint matching method, achieving a target reduction error of (3.265 ± 1.485) mm, rotation errors of (3.476 ± 1.995)°, and translation errors of (2.773 ± 1.390) mm. In the proof-of-concept cadaver studies, our method achieved a DSC of 0.988 in segmentation and 3.731 mm error in reduction planning, which senior experts deemed excellent. In conclusion, our automated approach significantly improves traditional preoperative planning, enhancing both efficiency and accuracy in pelvic fracture reduction.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103506"},"PeriodicalIF":10.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474420","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":"Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model","authors":"Lixuan Chen ,&nbsp;Xuanyu Tian ,&nbsp;Jiangjie Wu ,&nbsp;Ruimin Feng ,&nbsp;Guoyan Lao ,&nbsp;Yuyao Zhang ,&nbsp;Hongen Liao ,&nbsp;Hongjiang Wei","doi":"10.1016/j.media.2025.103502","DOIUrl":"10.1016/j.media.2025.103502","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it faces challenges such as long acquisition time and vulnerability to motion-induced artifacts. While many existing motion correction algorithms have shown success, most fail to account for the impact of motion artifacts on coil sensitivity map (CSM) estimation during fast MRI reconstruction. This oversight can lead to significant performance degradation, as errors in the estimated CSMs can propagate and compromise motion correction. In this work, we propose JSMoCo, a novel method for jointly estimating motion parameters and time-varying coil sensitivity maps for under-sampled MRI reconstruction. The joint estimation presents a highly ill-posed inverse problem due to the increased number of unknowns. To address this challenge, we leverage score-based diffusion models as powerful priors and apply MRI physical principles to effectively constrain the solution space. Specifically, we parameterize rigid motion with trainable variables and model CSMs as polynomial functions. A Gibbs sampler is employed to ensure system consistency between the sensitivity maps and the reconstructed images, effectively preventing error propagation from pre-estimated sensitivity maps to the final reconstructed images. We evaluate JSMoCo through 2D and 3D motion correction experiments on simulated motion-corrupted fastMRI dataset and <em>in-vivo</em> real MRI brain scans. The results demonstrate that JSMoCo successfully reconstructs high-quality MRI images from under-sampled k-space data, achieving robust motion correction by accurately estimating time-varying coil sensitivities. The code is available at <span><span>https://github.com/MeijiTian/JSMoCo</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103502"},"PeriodicalIF":10.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550698","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":"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":"Navigating prevalence shifts in image analysis algorithm deployment","authors":"Patrick Godau ,&nbsp;Piotr Kalinowski ,&nbsp;Evangelia Christodoulou ,&nbsp;Annika Reinke ,&nbsp;Minu Tizabi ,&nbsp;Luciana Ferrer ,&nbsp;Paul Jäger ,&nbsp;Lena Maier-Hein","doi":"10.1016/j.media.2025.103504","DOIUrl":"10.1016/j.media.2025.103504","url":null,"abstract":"<div><div>Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current research emphasizes new training methods and network architectures, the specific impact of prevalence shifts on algorithms in real-world applications is often overlooked. Differences in class frequencies between development and deployment data are crucial, particularly for the widespread adoption of artificial intelligence (AI), as disease prevalence can vary greatly across different times and locations. Our contribution is threefold. Based on a diverse set of 30 medical classification tasks (1) we demonstrate that lack of prevalence shift handling can have severe consequences on the quality of calibration, decision threshold, and performance assessment. Furthermore, (2) we show that prevalences can be accurately and reliably estimated in a data-driven manner. Finally, (3) we propose a new workflow for prevalence-aware image classification that uses estimated deployment prevalences to adjust a trained classifier to a new environment, without requiring additional annotated deployment data. Comprehensive experiments indicate that our proposed approach could contribute to generating better classifier decisions and more reliable performance estimates compared to current practice.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103504"},"PeriodicalIF":10.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508154","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":"Deep learning based coronary vessels segmentation in X-ray angiography using temporal information","authors":"Haorui He ,&nbsp;Abhirup Banerjee ,&nbsp;Robin P. Choudhury ,&nbsp;Vicente Grau","doi":"10.1016/j.media.2025.103496","DOIUrl":"10.1016/j.media.2025.103496","url":null,"abstract":"<div><div>Invasive coronary angiography (ICA) is the gold standard imaging modality during cardiac interventions. Accurate segmentation of coronary vessels in ICA is required for aiding diagnosis and creating treatment plans. Current automated algorithms for vessel segmentation face task-specific challenges, including motion artifacts and unevenly distributed contrast, as well as the general challenge inherent to X-ray imaging, which is the presence of shadows from overlapping organs in the background. To address these issues, we present Temporal Vessel Segmentation Network (TVS-Net) model that fuses sequential ICA information into a novel densely connected 3D encoder-2D decoder structure with a loss function based on elastic interaction. We develop our model using an ICA dataset comprising 323 samples, split into 173 for training, 82 for validation, and 68 for testing, with a relatively relaxed annotation protocol that produced coarse-grained samples, and achieve 83.4% Dice and 84.3% recall on the test dataset. We additionally perform an external evaluation over 60 images from a local hospital, achieving 78.5% Dice and 82.4% recall and outperforming the state-of-the-art approaches. We also conduct a detailed manual re-segmentation for evaluation only on a subset of the first dataset under strict annotation protocol, achieving a Dice score of 86.2% and recall of 86.3% and surpassing even the coarse-grained gold standard used in training. The results indicate our TVS-Net is effective for multi-frame ICA segmentation, highlights the network’s generalizability and robustness across diverse settings, and showcases the feasibility of weak supervision in ICA segmentation.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"102 ","pages":"Article 103496"},"PeriodicalIF":10.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550699","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}