Medical image analysis最新文献

{"title":"Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping","authors":"Qijian Chen ,&nbsp;Lihui Wang ,&nbsp;Zeyu Deng ,&nbsp;Rongpin Wang ,&nbsp;Li Wang ,&nbsp;Caiqing Jian ,&nbsp;Yue-Min Zhu","doi":"10.1016/j.media.2024.103435","DOIUrl":"10.1016/j.media.2024.103435","url":null,"abstract":"<div><div>Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging. To achieve this, we propose a novel cooperative multi-task learning network (CMTLNet) which consists of a task-common feature extraction (CFE) module, a task-specific unique feature extraction (UFE) module and a unique-common feature collaborative classification (UCFC) module. In CFE, a segmentation-free tumor feature perception (SFTFP) module is first designed to extract the tumor-aware features in a classification manner rather than a segmentation manner. Following that, based on the multi-scale tumor-aware features extracted by SFTFP module, CFE uses convolutional layers to further refine these features, from which the task-common features are learned. In UFE, based on orthogonal projection and conditional classification strategies, the task-specific unique features are extracted. In UCFC, the unique and common features are fused with an attention mechanism to make them adaptive to different glioma prediction tasks. Finally, deep features-guided interpretable radiomic biomarkers for each glioma prediction task are explored by combining SHAP values and correlation analysis. Through the comparisons with recent reported methods on a large multi-center dataset comprising over 1800 cases, we demonstrated the superiority of the proposed CMTLNet, with the mean Matthews correlation coefficient in validation and test sets improved by (4.1%, 10.7%), (3.6%, 23.4%), and (2.7%, 22.7%) respectively for glioma grading, 1p/19q and IDH status prediction tasks. In addition, we found that some radiomic features are highly related to uninterpretable deep features and that their variation trends are consistent in multi-center datasets, which can be taken as reliable imaging biomarkers for glioma diagnosis. The proposed CMTLNet provides an interpretable tool for glioma multi-task prediction, which is beneficial for glioma precise diagnosis and personalized treatment.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103435"},"PeriodicalIF":10.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950922","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":"2D echocardiography video to 3D heart shape reconstruction for clinical application","authors":"Fabian Laumer ,&nbsp;Lena Rubi ,&nbsp;Michael A. Matter ,&nbsp;Stefano Buoso ,&nbsp;Gabriel Fringeli ,&nbsp;François Mach ,&nbsp;Frank Ruschitzka ,&nbsp;Joachim M. Buhmann ,&nbsp;Christian M. Matter","doi":"10.1016/j.media.2024.103434","DOIUrl":"10.1016/j.media.2024.103434","url":null,"abstract":"<div><div>Transthoracic Echocardiography (TTE) is a crucial tool for assessing cardiac morphology and function quickly and non-invasively without ionising radiation. However, the examination is subject to intra- and inter-user variability and recordings are often limited to 2D imaging and assessments of end-diastolic and end-systolic volumes. We have developed a novel, fully automated machine learning-based framework to generate a personalised 4D (3D plus time) model of the left ventricular (LV) blood pool with high temporal resolution. A 4D shape is reconstructed from specific 2D echocardiographic views employing deep neural networks, pretrained on a synthetic dataset, and fine-tuned in a self-supervised manner using a novel optimisation method for cross-sectional imaging data. No 3D ground truth is needed for model training. The generated <em>digital twins</em> enhance the interpretation of TTE data by providing a versatile tool for automated analysis of LV volume changes, localisation of infarct areas, and identification of new and clinically relevant biomarkers. Experiments are performed on a multicentre dataset that includes TTE exams of 144 patients with normal TTE and 314 patients with acute myocardial infarction (AMI). The novel biomarkers show a high predictive value for survival (area under the curve (AUC) of 0.82 for 1-year all-cause mortality), demonstrating that personalised 3D shape modelling has the potential to improve diagnostic accuracy and risk assessment.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103434"},"PeriodicalIF":10.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910004","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":"Cross-center Model Adaptive Tooth segmentation","authors":"Ruizhe Chen ,&nbsp;Jianfei Yang ,&nbsp;Huimin Xiong ,&nbsp;Ruiling Xu ,&nbsp;Yang Feng ,&nbsp;Jian Wu ,&nbsp;Zuozhu Liu","doi":"10.1016/j.media.2024.103443","DOIUrl":"10.1016/j.media.2024.103443","url":null,"abstract":"<div><div>Automatic 3-dimensional tooth segmentation on intraoral scans (IOS) plays a pivotal role in computer-aided orthodontic treatments. In practice, deploying existing well-trained models to different medical centers suffers from two main problems: (1) the data distribution shifts between existing and new centers, which causes significant performance degradation. (2) The data in the existing center(s) is usually not permitted to be shared, and annotating additional data in the new center(s) is time-consuming and expensive, thus making re-training or fine-tuning unfeasible. In this paper, we propose a framework for Cross-center Model Adaptive Tooth segmentation (CMAT) to alleviate these issues. CMAT takes the trained model(s) from the source center(s) as input and adapts them to different target centers, without data transmission or additional annotations. CMAT is applicable to three cross-center scenarios: source-data-free, multi-source-data-free, and test-time. The model adaptation in CMAT is realized by a tooth-level prototype alignment module, a progressive pseudo-labeling transfer module, and a tooth-prior regularized information maximization module. Experiments under three cross-center scenarios on two datasets show that CMAT can consistently surpass existing baselines. The effectiveness is further verified with extensive ablation studies and statistical analysis, demonstrating its applicability for privacy-preserving model adaptive tooth segmentation in real-world digital dentistry.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103443"},"PeriodicalIF":10.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950923","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 lightweight generative model for interpretable subject-level prediction","authors":"Chiara Mauri ,&nbsp;Stefano Cerri ,&nbsp;Oula Puonti ,&nbsp;Mark Mühlau ,&nbsp;Koen Van Leemput","doi":"10.1016/j.media.2024.103436","DOIUrl":"10.1016/j.media.2024.103436","url":null,"abstract":"<div><div>Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject’s diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable. It augments the generative models used in classical human brain mapping techniques, in which the underlying cause–effect relations can be encoded, with a multivariate noise model that captures dominant spatial correlations. Experiments demonstrate that the resulting model can be efficiently inverted to make accurate subject-level predictions, while at the same time offering intuitive visual explanations of its inner workings. The method is easy to use: training is fast for typical training set sizes, and only a single hyperparameter needs to be set by the user. Our code is available at <span><span>https://github.com/chiara-mauri/Interpretable-subject-level-prediction</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103436"},"PeriodicalIF":10.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965948","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":"Learnable color space conversion and fusion for stain normalization in pathology images","authors":"Jing Ke ,&nbsp;Yijin Zhou ,&nbsp;Yiqing Shen ,&nbsp;Yi Guo ,&nbsp;Ning Liu ,&nbsp;Xiaodan Han ,&nbsp;Dinggang Shen","doi":"10.1016/j.media.2024.103424","DOIUrl":"10.1016/j.media.2024.103424","url":null,"abstract":"<div><div>Variations in hue and contrast are common in H&amp;E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant challenges for computer-assisted diagnostic systems, leading to potential underdiagnosis or misdiagnosis, especially when stain differentiation introduces substantial heterogeneity across datasets from different sources. Traditional stain normalization methods, aimed at mitigating these issues, often require labor-intensive selection of appropriate templates, limiting their practicality and automation. Innovatively, we propose a Learnable Stain Normalization layer, <em>i</em>.<em>e</em>. <span><math><mrow><mi>L</mi><mi>S</mi><mi>t</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>N</mi><mi>o</mi><mi>r</mi><mi>m</mi></mrow></math></span>, designed as an easily integrable component for pathology image analysis. It minimizes the need for manual template selection by autonomously learning the optimal stain characteristics. Moreover, the learned optimal stain template provides the interpretability to enhance the understanding of the normalization process. Additionally, we demonstrate that fusing pathology images normalized in multiple color spaces can improve performance. Therefore, we extend <span><math><mrow><mi>L</mi><mi>S</mi><mi>t</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>N</mi><mi>o</mi><mi>r</mi><mi>m</mi></mrow></math></span> with a novel self-attention mechanism to facilitate the fusion of features across different attributes and color spaces. Experimentally, <span><math><mrow><mi>L</mi><mi>S</mi><mi>t</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>N</mi><mi>o</mi><mi>r</mi><mi>m</mi></mrow></math></span> outperforms the state-of-the-art methods including conventional ones and GANs on two classification datasets and three nuclei segmentation datasets by an average increase of 4.78% in accuracy, 3.53% in Dice coefficient, and 6.59% in IoU. Additionally, by enabling an end-to-end training and inference process, <span><math><mrow><mi>L</mi><mi>S</mi><mi>t</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>N</mi><mi>o</mi><mi>r</mi><mi>m</mi></mrow></math></span> eliminates the need for intermediate steps between normalization and analysis, resulting in more efficient use of hardware resources and significantly faster inference time, <em>i</em>.<em>e</em> up to hundreds of times quicker than traditional methods. The code is publicly available at <span><span>https://github.com/yjzscode/Optimal-Normalisation-in-Color-Spaces</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103424"},"PeriodicalIF":10.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910006","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":"SurgiTrack: Fine-grained multi-class multi-tool tracking in surgical videos","authors":"Chinedu Innocent Nwoye,&nbsp;Nicolas Padoy","doi":"10.1016/j.media.2024.103438","DOIUrl":"10.1016/j.media.2024.103438","url":null,"abstract":"<div><div>Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of surgical procedures, especially tracking scenarios like out-of-body and out-of-camera views. Addressing this limitation, the new CholecTrack20 dataset provides detailed labels that account for multiple tool trajectories in three <em>perspectives</em>: (1) intraoperative, (2) intracorporeal, and (3) visibility, representing the different types of temporal duration of tool tracks. These fine-grained labels enhance tracking flexibility but also increase the task complexity. Re-identifying tools after occlusion or re-insertion into the body remains challenging due to high visual similarity, especially among tools of the same category. This work recognizes the critical role of the tool operators in distinguishing tool track instances, especially those belonging to the same tool category. The operators’ information are however not explicitly captured in surgical videos. We therefore propose SurgiTrack, a novel deep learning method that leverages YOLOv7 for precise tool detection and employs an attention mechanism to model the originating direction of the tools, as a proxy to their operators, for tool re-identification. To handle diverse tool trajectory perspectives, SurgiTrack employs a harmonizing bipartite matching graph, minimizing conflicts and ensuring accurate tool identity association. Experimental results on CholecTrack20 demonstrate SurgiTrack’s effectiveness, outperforming baselines and state-of-the-art methods with real-time inference capability. This work sets a new standard in surgical tool tracking, providing dynamic trajectories for more adaptable and precise assistance in minimally invasive surgeries.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103438"},"PeriodicalIF":10.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872471","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":"Personalized dental crown design: A point-to-mesh completion network","authors":"Golriz Hosseinimanesh ,&nbsp;Ammar Alsheghri ,&nbsp;Julia Keren ,&nbsp;Farida Cheriet ,&nbsp;Francois Guibault","doi":"10.1016/j.media.2024.103439","DOIUrl":"10.1016/j.media.2024.103439","url":null,"abstract":"<div><div>Designing dental crowns with computer-aided design software in dental laboratories is complex and time-consuming. Using real clinical datasets, we developed an end-to-end deep learning model that automatically generates personalized dental crown meshes. The input context includes the prepared tooth, its adjacent teeth, and the two closest teeth in the opposing jaw. The training set contains this context, the ground truth crown, and the extracted margin line. Our model consists of two components: First, a feature extractor converts the input point cloud into a set of local feature vectors, which are then fed into a transformer-based model to predict the geometric features of the crown. Second, a point-to-mesh module generates a dense array of points with normal vectors, and a differentiable Poisson surface reconstruction method produces an accurate crown mesh. Training is conducted with three losses: (1) a customized margin line loss; (2) a contrastive-based Chamfer distance loss; and (3) a mean square error (MSE) loss to control mesh quality. We compare our method with our previously published method, Dental Mesh Completion (DMC). Extensive testing confirms our method’s superiority, achieving a 12.32% reduction in Chamfer distance and a 46.43% reduction in MSE compared to DMC. Margin line loss improves Chamfer distance by 5.59%.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103439"},"PeriodicalIF":10.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872391","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":"Brain networks and intelligence: A graph neural network based approach to resting state fMRI data","authors":"Bishal Thapaliya ,&nbsp;Esra Akbas ,&nbsp;Jiayu Chen ,&nbsp;Ram Sapkota ,&nbsp;Bhaskar Ray ,&nbsp;Pranav Suresh ,&nbsp;Vince D. Calhoun ,&nbsp;Jingyu Liu","doi":"10.1016/j.media.2024.103433","DOIUrl":"10.1016/j.media.2024.103433","url":null,"abstract":"<div><div>Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence. Our GitHub implementation is publicly available on <span><span>https://github.com/bishalth01/BrainRGIN/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103433"},"PeriodicalIF":10.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872360","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":"DDoCT: Morphology preserved dual-domain joint optimization for fast sparse-view low-dose CT imaging","authors":"Linxuan Li ,&nbsp;Zhijie Zhang ,&nbsp;Yongqing Li ,&nbsp;Yanxin Wang ,&nbsp;Wei Zhao","doi":"10.1016/j.media.2024.103420","DOIUrl":"10.1016/j.media.2024.103420","url":null,"abstract":"<div><div>Computed tomography (CT) is continuously becoming a valuable diagnostic technique in clinical practice. However, the radiation dose exposure in the CT scanning process is a public health concern. Within medical diagnoses, mitigating the radiation risk to patients can be achieved by reducing the radiation dose through adjustments in tube current and/or the number of projections. Nevertheless, dose reduction introduces additional noise and artifacts, which have extremely detrimental effects on clinical diagnosis and subsequent analysis. In recent years, the feasibility of applying deep learning methods to low-dose CT (LDCT) imaging has been demonstrated, leading to significant achievements. This article proposes a dual-domain joint optimization LDCT imaging framework (termed DDoCT) which uses noisy sparse-view projection to reconstruct high-performance CT images with joint optimization in projection and image domains. The proposed method not only addresses the noise introduced by reducing tube current, but also pays special attention to issues such as streak artifacts caused by a reduction in the number of projections, enhancing the applicability of DDoCT in practical fast LDCT imaging environments. Experimental results have demonstrated that DDoCT has made significant progress in reducing noise and streak artifacts and enhancing the contrast and clarity of the images.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103420"},"PeriodicalIF":10.7,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872369","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":"AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT","authors":"Chunming Li ,&nbsp;Yuchuan Qiao ,&nbsp;Wei Yu ,&nbsp;Yingguang Li ,&nbsp;Yankai Chen ,&nbsp;Zehao Fan ,&nbsp;Runguo Wei ,&nbsp;Botao Yang ,&nbsp;Zhiqing Wang ,&nbsp;Xuesong Lu ,&nbsp;Lianglong Chen ,&nbsp;Carlos Collet ,&nbsp;Miao Chu ,&nbsp;Shengxian Tu","doi":"10.1016/j.media.2024.103432","DOIUrl":"10.1016/j.media.2024.103432","url":null,"abstract":"<div><div>Coronary artery disease (CAD) is the leading cause of death globally. The 3D fusion of coronary X-ray angiography (XA) and optical coherence tomography (OCT) provides complementary information to appreciate coronary anatomy and plaque morphology. This significantly improve CAD diagnosis and prognosis by enabling precise hemodynamic and computational physiology assessments. The challenges of fusion lie in the potential misalignment caused by the foreshortening effect in XA and non-uniform acquisition of OCT pullback. Moreover, the need for reconstructions of major bifurcations is technically demanding. This paper proposed an automated 3D fusion framework AutoFOX, which consists of deep learning model TransCAN for 3D vessel alignment. The 3D vessel contours are processed as sequential data, whose features are extracted and integrated with bifurcation information to enhance alignment via a multi-task fashion. TransCAN shows the highest alignment accuracy among all methods with a mean alignment error of 0.99 ± 0.81 mm along the vascular sequence, and only 0.82 ± 0.69 mm at key anatomical positions. The proposed AutoFOX framework uniquely employs an advanced side branch lumen reconstruction algorithm to enhance the assessment of bifurcation lesions. A multi-center dataset is utilized for independent external validation, using the paired 3D coronary computer tomography angiography (CTA) as the reference standard. Novel morphological metrics are proposed to evaluate the fusion accuracy. Our experiments show that the fusion model generated by AutoFOX exhibits high morphological consistency with CTA. AutoFOX framework enables automatic and comprehensive assessment of CAD, especially for the accurate assessment of bifurcation stenosis, which is of clinical value to guiding procedure and optimization.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"101 ","pages":"Article 103432"},"PeriodicalIF":10.7,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864791","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}