Computerized Medical Imaging and Graphics最新文献

{"title":"Defect-adaptive landmark detection in pelvis CT images via personalized structure-aware learning","authors":"Xirui Zhao ,&nbsp;Deqiang Xiao ,&nbsp;Teng Zhang ,&nbsp;Jingfan Fan ,&nbsp;Danni Ai ,&nbsp;Tianyu Fu ,&nbsp;Yucong Lin ,&nbsp;Long Shao ,&nbsp;Hong Song ,&nbsp;Junqiang Wang ,&nbsp;Jian Yang","doi":"10.1016/j.compmedimag.2025.102693","DOIUrl":"10.1016/j.compmedimag.2025.102693","url":null,"abstract":"<div><div>Accurate localization of anatomical landmarks from pelvic CT images is crucial for preoperative planning in orthopedic procedures. However, existing automatic methods often underperform when facing defective bone structures, which are common in clinical scenarios involving trauma, resection, or severe degeneration. To address this challenge, we propose DADNet, a defect-adaptive detection network that incorporates personalized structural priors to achieve accurate and robust landmark detection in defective pelvis CT images. DADNet first constructs a structure-aware soft prior map that encodes the spatial distribution of landmarks based on the individual bone anatomy. This prior map, which highlights landmark-related regions, is generated via a dedicated convolutional module followed by logarithmic transformation. Guided by this soft prior, we extract local patches around the candidate regions and performs landmark regression using a patch-based context-aware detection network. To further enhance detection robustness in defective regions, we introduce a bone-aware detection loss that modulates the prediction confidence based on bone structures. The modulation weight is dynamically adjusted during training via a sigmoid scheduler, enabling progressive adaptation from coarse to fine structural constraints. We evaluate DADNet on both public and private datasets featuring varying degrees of pelvic defects. Our approach achieves an average detection error of 1.252 ± 0.075 mm on severely defective cases, significantly outperforming existing methods. The proposed framework demonstrates strong adaptability to anatomical variability and structural incompleteness, offering a promising tool for accurate and robust landmark detection in challenging clinical cases.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102693"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884165","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":"Temporally-aware diffusion model for brain progression modelling with bidirectional temporal regularisation","authors":"Mattia Litrico ,&nbsp;Francesco Guarnera ,&nbsp;Mario Valerio Giuffrida ,&nbsp;Daniele Ravì ,&nbsp;Sebastiano Battiato","doi":"10.1016/j.compmedimag.2025.102688","DOIUrl":"10.1016/j.compmedimag.2025.102688","url":null,"abstract":"<div><div>Generating realistic MRIs to accurately predict future changes in the structure of brain is an invaluable tool for clinicians in assessing clinical outcomes and analysing the disease progression at the patient level. However, current existing methods present some limitations: (i) some approaches fail to explicitly capture the relationship between structural changes and time intervals, especially when trained on age-imbalanced datasets; (ii) others rely only on scan interpolation, which lack clinical utility, as they generate intermediate images between timepoints rather than future pathological progression; and (iii) most approaches rely on 2D slice-based architectures, thereby disregarding full 3D anatomical context, which is essential for accurate longitudinal predictions. We propose a 3D Temporally-Aware Diffusion Model (TADM-3D), which accurately predicts brain progression on MRI volumes. To better model the relationship between time interval and brain changes, TADM-3D uses a pre-trained <em>Brain-Age Estimator</em> (BAE) that guides the diffusion model in the generation of MRIs that accurately reflect the expected age difference between baseline and generated follow-up scans. Additionally, to further improve the temporal awareness of TADM-3D, we propose the <em>Back-In-Time Regularisation</em> (BITR), by training TADM-3D to predict bidirectionally from the baseline to follow-up (forward), as well as from the follow-up to baseline (backward). Although predicting past scans has limited clinical applications, this regularisation helps the model generate temporally more accurate scans. We train and evaluate TADM-3D on the OASIS-3 dataset, and we validate the generalisation performance on an external test set from the NACC dataset. The code is available at <span><span>https://github.com/MattiaLitrico/TADM-3D</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102688"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866649","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":"Spectral-X: Latent prior enhanced spectral CT restoration with mamba-assisted X-net","authors":"Yikun Zhang ,&nbsp;Jiashun Wang ,&nbsp;Xi Wang ,&nbsp;Xu Ji ,&nbsp;Kai Chen ,&nbsp;Jian Yang ,&nbsp;Yinsheng Li ,&nbsp;Yang Chen","doi":"10.1016/j.compmedimag.2025.102696","DOIUrl":"10.1016/j.compmedimag.2025.102696","url":null,"abstract":"<div><div>Compared with conventional computed tomography (CT), spectral CT can simultaneously visualize internal structures and characterize the material composition of scanned objects by acquiring data at different energy spectra. Photon-counting CT (PCCT) and multi-source CT (MSCT) are two promising implementations of spectral CT. Besides, radiation exposure remains a long-standing concern in CT imaging, as excessive X-ray exposure may lead to genetic and cellular damage. For PCCT and MSCT, the radiation dose can be reduced by lowering the tube current and adopting complementary limited-view scanning, respectively. To mitigate the noise and artifacts induced by low-dose acquisition protocols, this paper proposes a Mamba-assisted X-Net leveraging latent priors for spectral CT, termed Spectral-X. First, considering the intrinsic characteristics of spectral CT, Spectral-X exploits the latent representation of the enhanced full-spectrum prior image to facilitate the restoration of multi-energy CT (MECT). Second, Spectral-X employs an X-shaped network with feature fusion blocks to adaptively capture and leverage multi-scale prior information in the latent space. Third, Spectral-X integrates a novel all-around Mamba mechanism that can efficiently model long-range dependencies, thereby enhancing the performance of the image restoration backbone network. Spectral-X is evaluated on both PCCT denoising and limited-view MSCT restoration tasks, and the experimental results demonstrate that Spectral-X achieves state-of-the-art performance in noise suppression, artifact removal, and structural restoration.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102696"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884164","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":"SAN-Wavelet cycleGAN for anatomical structure reinforcement and tissue detail preservation in abdominal CT synthesis","authors":"Yueyu Huang ,&nbsp;Lu Qiang ,&nbsp;Wenyu Xing ,&nbsp;Tao Jiang ,&nbsp;Yucheng He ,&nbsp;Yuan Liu ,&nbsp;Gaobo Zhang ,&nbsp;Jingxian Wang ,&nbsp;Xiaojun Song ,&nbsp;Yifang Li","doi":"10.1016/j.compmedimag.2025.102686","DOIUrl":"10.1016/j.compmedimag.2025.102686","url":null,"abstract":"<div><div>Generating a CT image from an existing but unpaired MR image has recently become a promising objective in the radiotherapy treatment planning (RTP), whereas the unsatisfactory structure and detail accuracy of the outcome is still a challenge for its clinical application. To address this issue, this work proposed an unsupervised model called structure-adaptive normalization wavelet (SAN-Wavelet) cycleGAN for unpaired MR-to-CT image synthesis. This method not only developed a module called SAN to ensure the anatomically structural integrity, but also introduced a neighborhood detail loss (ND loss) for detail consistency penalization between different modalities. Furthermore, a high-frequency discriminator and wavelet-trans skip connection were designed to keep with the high-frequency tissue detail. Experimental findings on abdominal area demonstrated the effectiveness of SAN-Wavelet cycleGAN for unpaired MR-to-CT synthesis, with mean squared error (MSE) of 66.38, root mean squared error (RMSE) of 8.07, peak signal-to-noise ratio (PSNR) of 25.944 dB, structural similarity index (SSlM) of 0.895 and mixture perceptual similarity index (MPSIM) of 0.723. Compared to other unsupervised approaches (i.e., cycleGAN, gc-, cc-, and sc-cycleGAN), the performances of SAN-cycleGAN improved by 5–15 % in terms of the metrics above. Moreover, the dosimetric distributions of the synthesized CT and real CT in planning target volume (PTV-45Gy) and organ at risk area (bowel) were statistically consistent (Mann-Whitney U test, P &gt; 0.05) with a mean relative error being 0.09 % and 0.24 %, showcasing a promising applicability for clinical application.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102686"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758295","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":"Wholistic report generation for Breast ultrasound using LangChain","authors":"Jaeyoung Huh ,&nbsp;Hye Shin Ahn ,&nbsp;Hyun Jeong Park ,&nbsp;Jong Chul Ye","doi":"10.1016/j.compmedimag.2025.102697","DOIUrl":"10.1016/j.compmedimag.2025.102697","url":null,"abstract":"<div><div>Breast ultrasound (BUS) is a vital imaging technique for detecting and characterizing breast abnormalities. Generating comprehensive BUS reports typically requires integrating multiple image views and patient information, which can be time-consuming for clinicians. This study explores the feasibility of a modular, AI-assisted framework to support BUS report generation, focusing on system integration. We developed a suite of classification networks for image analysis, coordinated via LangChain with Large Language Models (LLMs), to generate structured and clinically meaningful reports. A Retrieval-Augmented Generation (RAG) component allows the framework to incorporate prior patient information, enabling context-aware and personalized report generation. The system demonstrates the practical integration of existing image-analysis models and language-generation tools within a clinical workflow. Experimental evaluations show that the integrated framework produces consistent and clinically interpretable reports, which align well with radiologists’ assessments. These results suggest that the proposed approach provides a feasible, modular, and extensible solution for semi-automated BUS report generation, offering a foundation for further refinement and potential clinical deployment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102697"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884163","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":"Computerized medical imaging and graphics best paper award 2024.","authors":"Xiaoyin Xu, Stephen Tc Wong","doi":"10.1016/j.compmedimag.2025.102683","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2025.102683","url":null,"abstract":"","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"102683"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146020430","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":"Benchmarking pathology foundation models for predicting microsatellite instability in colorectal cancer histopathology","authors":"Mohsin Bilal ,&nbsp;Muhammad Aamir Gulzar ,&nbsp;Nazish Jaffar ,&nbsp;Abdulrahman Alabduljabbar ,&nbsp;Youssef Altherwy ,&nbsp;Anas Alsuhaibani ,&nbsp;Fahdah Almarshad","doi":"10.1016/j.compmedimag.2025.102680","DOIUrl":"10.1016/j.compmedimag.2025.102680","url":null,"abstract":"<div><div>The rapid evolution of pathology foundation models necessitates rigorous benchmarking for clinical tasks. We evaluated three leading foundation models, UNI, Virchow2, and CONCH, for predicting microsatellite instability status from colorectal cancer whole-slide images, an essential routine clinical test. Our comprehensive framework assessed stain, tissue, and resolution invariance using datasets from The Cancer Genome Atlas (TCGA, USA; n = 409) and Pathology Artificial Intelligence Platform (PAIP, South Korea; training n = 47, testing n = 21 and n = 78). We developed an efficient pipeline with minimal preprocessing, omitting stain normalization, color augmentation, and tumor segmentation. To improve contextual encoding, we applied a five-crop strategy per patch, averaging embeddings from the center and four peripheral crops. We compared three slide-level aggregation and four efficient adaptation strategies. CONCH, using 2-cluster aggregation and ProtoNet adaptation, achieved top balanced accuracies (0.775 and 0.778) in external validation on PAIP. Conversely, UNI, with mean-averaging aggregation and ANN adaptation, excelled in TCGA cross-validation (0.778) but not in external validation (0.764), suggesting potential overfitting. The proposed 5-Crop augmentation enhances robustness to scale in UNI and CONCH and reflects intrinsic invariance achieved by Virchow2 through large-scale pretraining. For prescreening, CONCH demonstrated specificity of 0.65 and 0.45 at sensitivities of 0.90 and 0.94, respectively, highlighting its effectiveness in identifying stable cases and minimizing number of rapid molecular tests needed. Interestingly, a fine-tuned ResNet34 adaptation achieved superior performance (0.836) in the smaller internal validation cohort, suggesting current pathology foundation models training recipes may not sufficiently generalize without task-specific fine-tuning. Interpretability analyses using CONCH’s multimodal embeddings identified plasma cells as key morphological features differentiating microsatellite instability from stability, validated by pathologists (accuracy up to 92.4 %). This study underscores the feasibility and clinical significance of adapting foundation models to enhance diagnostic efficiency and patient outcomes.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102680"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685281","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":"Multimodal framework for TACE treatment response prediction in patients with hepatocellular carcinoma","authors":"Pietro Maria Marvulli ,&nbsp;Domenico Buongiorno ,&nbsp;Gian Maria Zaccaria,&nbsp;Antonio Brunetti,&nbsp;Francescomaria Marino,&nbsp;Vitoantonio Bevilacqua","doi":"10.1016/j.compmedimag.2025.102679","DOIUrl":"10.1016/j.compmedimag.2025.102679","url":null,"abstract":"<div><div>Transarterial chemoembolization (TACE) is a first-line treatment for intermediate-stage hepatocellular carcinoma (HCC) that can cause side effects. An accurate prediction of TACE response is important to improve clinical outcomes and avoid unnecessary toxicity. This study pursues a dual objective: to propose a standardized evaluation pipeline that enables reproducible benchmarking of state-of-the-art approaches on publicly available datasets, including both internal and external validation with public dataset, and to introduce a novel multimodal framework that integrates clinical variables, radiomic and deep features extracted from CT scans using the Vision Transformer MedViT to predict treatment response. Experiments were conducted using two publicly available datasets, the HCC-TACE-Seg, used as training and internal validation sets, and the WAW-TACE cohort, used as external validation set. The results demonstrated that the proposed method outperforms existing approaches. Independent validation on the external WAW-TACE dataset achieved promising results, confirming the robustness of the model and its potential to support treatment planning.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102679"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679264","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":"KWC-YOLO: An efficient YOLO architecture for lumbar spinal stenosis grading through dynamic convolution and spatially-aware gating","authors":"Wei Chen ,&nbsp;Fan Wu ,&nbsp;Yan Guo ,&nbsp;Weiqing Zhuang ,&nbsp;Hua Chen ,&nbsp;Jie Feng ,&nbsp;Jianfeng Wu","doi":"10.1016/j.compmedimag.2025.102685","DOIUrl":"10.1016/j.compmedimag.2025.102685","url":null,"abstract":"<div><div>Lumbar spinal stenosis (LSS) represents a significant global health burden, and its diagnosis from Magnetic Resonance Imaging (MRI) is often subject to inter-observer variability and time-consuming interpretation. While deep learning (DL) models offer a promising solution, they are frequently constrained by the scarcity of annotated medical data, high computational demands, and challenges in representing subtle pathological features. To address these limitations, we propose KWC-YOLO, a novel and efficient object detection framework for the automated detection and classification of lumbar central canal stenosis (LCCS) severity according to the Schizas grading criteria. Our model enhances the YOLOv11n architecture through three core innovations: (1) the integration of KernelWarehouse (KWConv), a parameter-efficient dynamic convolution mechanism that improves the feature adaptability of the detection head; (2) the introduction of a FasterGATE activation unit in the backbone to enhance non-linear representation and accelerate convergence; and (3) the implementation of a lightweight Slim-Neck structure, which optimizes the trade-off between feature fusion quality and computational cost. On a clinical lumbar spine MRI dataset, KWC-YOLO demonstrates superior performance, achieving a mean Average Precision at an IoU of 0.5 (<span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span>) of 86.7% and an <span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>95</mn></mrow></msub></mrow></math></span> of 63.0%. This represents a substantial improvement over the YOLOv11n baseline by 9.2 and 9.3 percentage points in <span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span> and <span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>95</mn></mrow></msub></mrow></math></span> respectively, while simultaneously reducing the computational load by 36.5% to 4.0 GFLOPs. Conclusively, KWC-YOLO establishes a new benchmark for automated LCCS grading. Its compelling balance of high accuracy and computational efficiency holds the potential to alleviate the interpretative burden on radiologists, enhance reporting accuracy, and streamline clinical decision-making, ultimately leading to improved patient outcomes.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102685"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840029","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 spatiotemporal clutter filtering of transthoracic echocardiographic images: Leveraging contextual attention and residual learning","authors":"Mahdi Tabassian ,&nbsp;Somayeh Akbari ,&nbsp;Sandro Queirós ,&nbsp;Lamia Al Saikhan ,&nbsp;Jan D’hooge","doi":"10.1016/j.compmedimag.2025.102665","DOIUrl":"10.1016/j.compmedimag.2025.102665","url":null,"abstract":"<div><div>This study presents a deep autoencoder network for filtering reverberation clutter from transthoracic echocardiographic (TTE) images. Given the spatiotemporal nature of this type of clutter, the filtering network employs 3D convolutional layers to suppress it throughout the cardiac cycle. The design of the network incorporates two key features that contribute to the effectiveness of the filter: (1) an <em>attention mechanism</em> for focusing on cluttered regions and leveraging contextual information, and (2) <em>residual learning</em> for preserving fine image structures. A diverse set of artifact patterns was simulated and superimposed onto ultra-realistic synthetic TTE sequences from six ultrasound vendors, generating input for the filtering network. The corresponding artifact-free sequences served as ground-truth. The performance of the filtering network was evaluated using unseen synthetic and <em>in vivo</em> artifactual sequences. Results from the <em>in vivo</em> dataset confirmed the network’s strong generalization capabilities, despite being trained solely on synthetic data and simulated artifacts. The suitability of the filtered sequences for downstream processing was assessed by computing segmental strain curves. A significant reduction in the discrepancy between the strain profiles of the cluttered and clutter-free segments was observed after filtering. The trained network processes a TTE sequence in a fraction of a second, enabling real-time clutter filtering and potentially improving the precision of clinically relevant indices derived from TTE sequences. The source code of the proposed method and example video files of the filtering results are available at: <span><span>https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"127 ","pages":"Article 102665"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662682","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}