Computerized Medical Imaging and Graphics最新文献

{"title":"Path and bone-contour regularized unpaired MRI-to-CT translation","authors":"Teng Zhou ,&nbsp;Jax Luo ,&nbsp;Yuping Sun ,&nbsp;Yiheng Tan ,&nbsp;Shun Yao ,&nbsp;Nazim Haouchine ,&nbsp;Scott Raymond","doi":"10.1016/j.compmedimag.2025.102656","DOIUrl":"10.1016/j.compmedimag.2025.102656","url":null,"abstract":"<div><div>Accurate MRI-to-CT translation promises the integration of complementary imaging information without the need for additional imaging sessions. Given the practical challenges associated with acquiring paired MRI and CT scans, the development of robust methods capable of leveraging unpaired datasets is essential for advancing the MRI-to-CT translation. Current unpaired MRI-to-CT translation methods, which predominantly rely on cycle consistency and contrastive learning frameworks, frequently encounter challenges in accurately translating anatomical features that are highly discernible on CT but less distinguishable on MRI, such as bone structures. This limitation renders these approaches less suitable for applications in radiation therapy, where precise bone representation is essential for accurate treatment planning. To address this challenge, we propose a path- and bone-contour regularized approach for unpaired MRI-to-CT translation. In our method, MRI and CT images are projected to a shared latent space, where the MRI-to-CT mapping is modeled as a continuous flow governed by neural ordinary differential equations. The optimal mapping is obtained by minimizing the transition path length of the flow. To enhance the accuracy of translated bone structures, we introduce a trainable neural network to generate bone contours from MRI and implement mechanisms to directly and indirectly encourage the model to focus on bone contours and their adjacent regions. Evaluations conducted on three datasets demonstrate that our method outperforms existing unpaired MRI-to-CT translation approaches, achieving lower overall error rates. Moreover, in a downstream bone segmentation task, our approach exhibits superior performance in preserving the fidelity of bone structures. Our code is available at: <span><span>https://github.com/kennysyp/PaBoT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"126 ","pages":"Article 102656"},"PeriodicalIF":4.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESAM2-BLS: Enhanced segment anything model 2 for efficient breast lesion segmentation in ultrasound imaging","authors":"Lishuang Guo ,&nbsp;Haonan Zhang ,&nbsp;Chenbin Ma","doi":"10.1016/j.compmedimag.2025.102654","DOIUrl":"10.1016/j.compmedimag.2025.102654","url":null,"abstract":"<div><div>Ultrasound imaging, as an economical, efficient, and non-invasive diagnostic tool, is widely used for breast lesion screening and diagnosis. However, the segmentation of lesion regions remains a significant challenge due to factors such as noise interference and the variability in image quality. To address this issue, we propose a novel deep learning model named enhanced segment anything model 2 (SAM2) for breast lesion segmentation (ESAM2-BLS). This model is an optimized version of the SAM2 architecture. ESAM2-BLS customizes and fine-tunes the pre-trained SAM2 model by introducing an adapter module, specifically designed to accommodate the unique characteristics of breast ultrasound images. The adapter module directly addresses ultrasound-specific challenges including speckle noise, low contrast boundaries, shadowing artifacts, and anisotropic resolution through targeted architectural elements such as channel attention mechanisms, specialized convolution kernels, and optimized skip connections. This optimization significantly improves segmentation accuracy, particularly for low-contrast and small lesion regions. Compared to traditional methods, ESAM2-BLS fully leverages the generalization capabilities of large models while incorporating multi-scale feature fusion and axial dilated depthwise convolution to effectively capture multi-level information from complex lesions. During the decoding process, the model enhances the identification of fine boundaries and small lesions through depthwise separable convolutions and skip connections, while maintaining a low computational cost. Visualization of the segmentation results and interpretability analysis demonstrate that ESAM2-BLS achieves an average Dice score of 0.9077 and 0.8633 in five-fold cross-validation across two datasets with over 1600 patients. These results significantly improve segmentation accuracy and robustness. This model provides an efficient, reliable, and specialized automated solution for early breast cancer screening and diagnosis.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"126 ","pages":"Article 102654"},"PeriodicalIF":4.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends and applications of variational autoencoders in medical imaging analysis","authors":"Pauline Shan Qing Yeoh ,&nbsp;Khairunnisa Hasikin ,&nbsp;Xiang Wu ,&nbsp;Siew Li Goh ,&nbsp;Khin Wee Lai","doi":"10.1016/j.compmedimag.2025.102647","DOIUrl":"10.1016/j.compmedimag.2025.102647","url":null,"abstract":"<div><div>Automated medical imaging analysis plays a crucial role in modern healthcare, with deep learning emerging as a widely adopted solution. However, traditional supervised learning methods often struggle to achieve optimal performance due to increasing challenges such as data scarcity and variability. In response, generative artificial intelligence has gained significant attention, particularly Variational Autoencoders (VAEs), which have been extensively utilized to address various challenges in medical imaging. This review analyzed 118 articles published in the Web of Science database between 2018 and 2024. Bibliometric analysis was conducted to map research trends, while a curated compilation of datasets and evaluation metrics were extracted to underscore the importance of standardization in deep learning workflows. VAEs have been applied across multiple healthcare applications, including anomaly detection, segmentation, classification, synthesis, registration, harmonization, and clustering. Findings suggest that VAE-based models are increasingly applied in medical imaging, with Magnetic Resonance Imaging emerging as the dominant modality and image synthesis as a primary application. The growing interest in this field highlights the potential of VAEs to enhance medical imaging analysis by overcoming existing limitations in data-driven healthcare solutions. This review serves as a valuable resource for researchers looking to integrate VAE models into healthcare applications, offering an overview of current advancements.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"126 ","pages":"Article 102647"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twin-ViMReg: DXR driven synthetic dynamic Standing-CBCTs through Twin Vision Mamba-based 2D/3D registration","authors":"Jiashun Wang ,&nbsp;Hao Tang ,&nbsp;Zhan Wu ,&nbsp;Yikun Zhang ,&nbsp;Yan Xi ,&nbsp;Yang Chen ,&nbsp;Chunfeng Yang ,&nbsp;Yixin Zhou ,&nbsp;Hui Tang","doi":"10.1016/j.compmedimag.2025.102648","DOIUrl":"10.1016/j.compmedimag.2025.102648","url":null,"abstract":"<div><div>Medical imaging of the knee joint under physiological weight bearing is crucial for diagnosing and analyzing knee lesions. Existing modalities have limitations: Standing Cone-Beam Computed Tomography (Standing-CBCT) provides high-resolution 3D data but with long acquisition time and only a single static view, while Dynamic X-ray Imaging (DXR) captures continuous motion but lacks 3D structural information. These limitations motivate the need for dynamic 3D knee generation through 2D/3D registration of Standing-CBCT and DXR. Anatomically, although the femur, patella, and tibia–fibula undergo rigid motion, the joint as a whole exhibits non-rigid behavior. Consequently, existing rigid or non-rigid 2D/3D registration methods fail to fully address this scenario. We propose Twin-ViMReg, a twin-stream 2D/3D registration framework for multiple correlated objects in the knee joint. It extends conventional 2D/3D registration paradigm by establishing a pair of twined sub-tasks. By introducing a Multi-Objective Spatial Transformation (MOST) module, it models inter-object correlations and enhances registration robustness. The Vision Mamba-based encoder also strengthens the representation capacity of the method. We used 1,500 simulated data pairs from 10 patients for training and 56 real data pairs from 3 patients for testing. Quantitative evaluation shows that the mean TRE reached 3.36 mm, the RSR was 8.93% higher than the SOTA methods. With an average computation time of 1.22 s per X-ray image, Twin-ViMReg enables efficient 2D/3D knee joint registration within seconds, making it a practical and promising solution.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102648"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collect vascular specimens in one cabinet: A hierarchical prompt-guided universal model for 3D vascular segmentation","authors":"Yinuo Wang ,&nbsp;Cai Meng ,&nbsp;Zhe Xu","doi":"10.1016/j.compmedimag.2025.102650","DOIUrl":"10.1016/j.compmedimag.2025.102650","url":null,"abstract":"<div><div>Accurate segmentation of vascular structures in volumetric medical images is critical for disease diagnosis and surgical planning. While deep neural networks have shown remarkable effectiveness, existing methods often rely on separate models tailored to specific modalities and anatomical regions, resulting in redundant parameters and limited generalization. Recent universal models address broader segmentation tasks but struggle with the unique challenges of vascular structures. To overcome these limitations, we first present <strong>VasBench</strong>, a new comprehensive vascular segmentation benchmark comprising nine sub-datasets spanning diverse modalities and anatomical regions. Building on this foundation, we introduce <strong>VasCab</strong>, a novel prompt-guided universal model for volumetric vascular segmentation, designed to “collect vascular specimens in one cabinet”. Specifically, VasCab is equipped with learnable domain and topology prompts to capture shared and unique vascular characteristics across diverse data domains, complemented by morphology perceptual loss to address complex morphological variations. Experimental results demonstrate that VasCab surpasses individual models and state-of-the-art medical foundation models across all test datasets, showcasing exceptional cross-domain integration and precise modeling of vascular morphological variations. Moreover, VasCab exhibits robust performance in downstream tasks, underscoring its versatility and potential for unified vascular analysis. This study marks a significant step toward universal vascular segmentation, offering a promising solution for unified vascular analysis across heterogeneous datasets. Code and dataset are available at <span><span>https://github.com/mileswyn/VasCab</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102650"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing intracranial vessel segmentation using diffusion models without manual annotation for 3D Time-of-Flight Magnetic Resonance Angiography","authors":"Jonghun Kim ,&nbsp;Inye Na ,&nbsp;Jiwon Chung ,&nbsp;Ha-Na Song ,&nbsp;Kyungseo Kim ,&nbsp;Seongvin Ju ,&nbsp;Mi-Yeon Eun ,&nbsp;Woo-Keun Seo ,&nbsp;Hyunjin Park","doi":"10.1016/j.compmedimag.2025.102651","DOIUrl":"10.1016/j.compmedimag.2025.102651","url":null,"abstract":"<div><div>Intracranial vessel segmentation is essential for managing brain disorders, facilitating early detection and precise intervention of stroke and aneurysm. Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) is a commonly used vascular imaging technique for segmenting brain vessels. Traditional rule-based MRA segmentation methods were efficient, but suffered from instability and poor performance. Deep learning models, including diffusion models, have recently gained attention in medical image segmentation. However, they require ground truth for training, which is labor-intensive and time-consuming to obtain. We propose a novel segmentation method that combines the strengths of rule-based and diffusion models to improve segmentation without relying on explicit labels. Our model adopts a Frangi filter to help with vessel detection and modifies the diffusion models to exclude memory-intensive attention modules to improve efficiency. Our condition network concatenates the feature maps to further enhance the segmentation process. Quantitative and qualitative evaluations on two datasets demonstrate that our approach not only maintains the integrity of the vascular regions but also substantially reduces noise, offering a robust solution for segmenting intracranial vessels. Our results suggest a basis for improved patient care in disorders involving brain vessels. Our code is available at <span><span>github.com/jongdory/Vessel-Diffusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102651"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary artery calcification segmentation with sparse annotations in intravascular OCT: Leveraging self-supervised learning and consistency regularization","authors":"Chao Li ,&nbsp;Zhifeng Qin ,&nbsp;Zhenfei Tang ,&nbsp;Yidan Wang ,&nbsp;Bo Zhang ,&nbsp;Jinwei Tian ,&nbsp;Zhao Wang","doi":"10.1016/j.compmedimag.2025.102653","DOIUrl":"10.1016/j.compmedimag.2025.102653","url":null,"abstract":"<div><div>Assessing coronary artery calcification (CAC) is crucial in evaluating the progression of atherosclerosis and planning percutaneous coronary intervention (PCI). Intravascular Optical Coherence Tomography (OCT) is a commonly used imaging tool for evaluating CAC at micrometer-scale level and in three-dimensions for optimizing PCI. While existing deep learning methods have proven effective in OCT image analysis, they are hindered by the lack of large-scale, high-quality labels to train deep neural networks that can reach human level performance in practice. In this work, we propose an annotation-efficient approach for segmenting CAC in intravascular OCT images, leveraging self-supervised learning and consistency regularization. We employ a transformer encoder paired with a simple linear projection layer for self-supervised pre-training on unlabeled OCT data. Subsequently, a transformer-based segmentation model is fine-tuned on sparsely annotated OCT pullbacks with a contrast loss using a combination of unlabeled and labeled data. We collected 2,549,073 unlabeled OCT images from 7,108 OCT pullbacks for pre-training, and 1,106,347 sparsely annotated OCT images from 3,025 OCT pullbacks for model training and testing. The proposed approach consistently outperformed existing sparsely supervised methods on both internal and external datasets. In addition, extensive comparisons under full, partial, and sparse annotation schemes substantiated its high annotation efficiency. With 80% reduction in image labeling efforts, our method has the potential to expedite the development of deep learning models for processing large-scale medical image data.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102653"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SA2Net: Scale-adaptive structure-affinity transformation for spine segmentation from ultrasound volume projection imaging","authors":"Hao Xie ,&nbsp;Zixun Huang ,&nbsp;Yushen Zuo ,&nbsp;Yakun Ju ,&nbsp;Frank H.F. Leung ,&nbsp;N.F. Law ,&nbsp;Kin-Man Lam ,&nbsp;Yong-Ping Zheng ,&nbsp;Sai Ho Ling","doi":"10.1016/j.compmedimag.2025.102649","DOIUrl":"10.1016/j.compmedimag.2025.102649","url":null,"abstract":"<div><div>Spine segmentation, based on ultrasound volume projection imaging (VPI), plays a vital role for intelligent scoliosis diagnosis in clinical applications. However, this task faces several significant challenges. Firstly, the global contextual knowledge of spines may not be well-learned if we neglect the high spatial correlation of different bone features. Secondly, the spine bones contain rich structural knowledge regarding their shapes and positions, which deserves to be encoded into the segmentation process. To address these challenges, we propose a novel scale-adaptive structure-aware network (SA²Net) for effective spine segmentation. First, we propose a scale-adaptive complementary strategy to learn the cross-dimensional long-distance correlation features for spinal images. Second, motivated by the consistency between multi-head self-attention in Transformers and semantic level affinity, we propose structure-affinity transformation to transform semantic features with class-specific affinity and combine it with a Transformer decoder for structure-aware reasoning. In addition, we adopt a feature mixing loss aggregation method to enhance model training. This method improves the robustness and accuracy of the segmentation process. The experimental results demonstrate that our SA²Net achieves superior segmentation performance compared to other state-of-the-art methods. Moreover, the adaptability of SA²Net to various backbones enhances its potential as a promising tool for advanced scoliosis diagnosis using intelligent spinal image analysis.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102649"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for automatic vertebra analysis: A methodological survey of recent advances","authors":"Zhuofan Xie ,&nbsp;Zishan Lin ,&nbsp;Enlong Sun ,&nbsp;Fengyi Ding ,&nbsp;Jie Qi ,&nbsp;Shen Zhao","doi":"10.1016/j.compmedimag.2025.102652","DOIUrl":"10.1016/j.compmedimag.2025.102652","url":null,"abstract":"<div><div>Automated vertebra analysis (AVA), encompassing vertebra detection and segmentation, plays a critical role in computer-aided diagnosis, surgical planning, and postoperative evaluation in spine-related clinical workflows. Despite notable progress, AVA continues to face key challenges, including variations in the field of view (FOV), complex vertebral morphology, limited availability of high-quality annotated data, and performance degradation under domain shifts. Over the past decade, numerous studies have employed deep learning (DL) to tackle these issues, introducing advanced network architectures and innovative learning paradigms. However, the rapid evolution of these methods has not been comprehensively captured by existing surveys, resulting in a knowledge gap regarding the current state of the field. To address this, this paper presents an up-to-date review that systematically summarizes recent advances. The review begins by consolidating publicly available datasets and evaluation metrics to support standardized benchmarking. Recent DL-based AVA approaches are then analyzed from two methodological perspectives: network architecture improvement and learning strategies design. Finally, an examination of persistent technical barriers and emerging clinical needs that are shaping future research directions is provided. These include multimodal learning, domain generalization, and the integration of foundation models. As the most current survey in the field, this review provides a comprehensive and structured synthesis aimed at guiding future research toward the development of robust, generalizable, and clinically deployable AVA systems in the era of intelligent medical imaging.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102652"},"PeriodicalIF":4.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SGRRG: Leveraging radiology scene graphs for improved and abnormality-aware radiology report generation","authors":"Jun Wang ,&nbsp;Lixing Zhu ,&nbsp;Abhir Bhalerao ,&nbsp;Yulan He","doi":"10.1016/j.compmedimag.2025.102644","DOIUrl":"10.1016/j.compmedimag.2025.102644","url":null,"abstract":"<div><div>Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. A scene graph provides comprehensive information for describing objects within an image. However, automatically generated radiology scene graphs (RSG) may contain noise annotations and highly overlapping regions, posing challenges in utilizing RSG to enhance RRG. To this end, we propose Scene Graph aided RRG (SGRRG), a framework that leverages an automatically generated RSG and copes with noisy supervision problems in the RSG with a transformer-based module, effectively distilling medical knowledge in an end-to-end manner. SGRRG is composed of a dedicated scene graph encoder responsible for translating the radiography into a RSG, and a scene graph-aided decoder that takes advantage of both patch-level and region-level visual information and mitigates the noisy annotation problem in the RSG. The incorporation of both patch-level and region-level features, alongside the integration of the essential RSG construction modules, enhances our framework’s flexibility and robustness, enabling it to readily exploit prior advanced RRG techniques. A fine-grained, sentence-level attention method is designed to better distill the RSG information. Additionally, we introduce two proxy tasks to enhance the model’s ability to produce clinically accurate reports. Extensive experiments demonstrate that SGRRG outperforms previous state-of-the-art methods in report generation and can better capture abnormal findings. Code is available at <span><span>https://github.com/Markin-Wang/SGRRG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102644"},"PeriodicalIF":4.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}