Computerized Medical Imaging and Graphics最新文献

{"title":"Meta-learning guidance for robust medical image synthesis: Addressing the real-world misalignment and corruptions","authors":"Jaehun Lee ,&nbsp;Daniel Kim ,&nbsp;Taehun Kim ,&nbsp;Mohammed A. Al-masni ,&nbsp;Yoseob Han ,&nbsp;Dong-Hyun Kim ,&nbsp;Kanghyun Ryu","doi":"10.1016/j.compmedimag.2025.102506","DOIUrl":"10.1016/j.compmedimag.2025.102506","url":null,"abstract":"<div><div>Deep learning-based image synthesis for medical imaging is currently an active research topic with various clinically relevant applications. Recently, methods allowing training with misaligned data have started to emerge, yet current solution lack robustness and cannot handle other corruptions in the dataset. In this work, we propose a solution to this problem for training synthesis network for datasets affected by mis-registration, artifacts, and deformations. Our proposed method consists of three key innovations: meta-learning inspired re-weighting scheme to directly decrease the influence of corrupted instances in a mini-batch by assigning lower weights in the loss function, non-local feature-based loss function, and joint training of image synthesis network together with spatial transformer (STN)-based registration networks with specially designed regularization. Efficacy of our method is validated in a controlled synthetic scenario, as well as public dataset with such corruptions. This work introduces a new framework that may be applicable to challenging scenarios and other more difficult datasets.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"121 ","pages":"Article 102506"},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjacent point aided vertebral landmark detection and Cobb angle measurement for automated AIS diagnosis","authors":"Xiaopeng Du ,&nbsp;Hongyu Wang ,&nbsp;Lihang Jiang ,&nbsp;Changlin Lv ,&nbsp;Yongming Xi ,&nbsp;Huan Yang","doi":"10.1016/j.compmedimag.2025.102496","DOIUrl":"10.1016/j.compmedimag.2025.102496","url":null,"abstract":"<div><div>Adolescent Idiopathic Scoliosis (AIS) is a prevalent structural deformity disease of human spine, and accurate assessment of spinal anatomical parameters is essential for clinical diagnosis and treatment planning. In recent years, significant progress has been made in automatic AIS diagnosis based on deep learning methods. However, effectively utilizing spinal structure information to improve the parameter measurement and diagnosis accuracy from spinal X-ray images remains challenging. This paper proposes a novel spine keypoint detection framework to complete the intelligent diagnosis of AIS, with the assistance of spine rigid structure information. Specifically, a deep learning architecture called Landmark and Adjacent offset Detection (LAD-Net) is designed to predict spine centre and corner points as well as their related offset vectors, based on which error-detected landmarks can be effectively corrected via the proposed Adjacent Centre Iterative Correction (ACIC) and Corner Feature Optimization and Fusion (CFOF) modules. Based on the detected spine landmarks, spine key parameters (<em>i.e</em>. Cobb angles) can be computed to finish the AIS Lenke diagnosis. Experimental results demonstrate the superiority of the proposed framework on spine landmark detection and Lenke classification, providing strong support for AIS diagnosis and treatment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"121 ","pages":"Article 102496"},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179161","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 Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing","authors":"Mingfu Jiang ,&nbsp;Shuai Wang ,&nbsp;Ka-Hou Chan ,&nbsp;Yue Sun ,&nbsp;Yi Xu ,&nbsp;Zhuoneng Zhang ,&nbsp;Qinquan Gao ,&nbsp;Zhifan Gao ,&nbsp;Tong Tong ,&nbsp;Hing-Chiu Chang ,&nbsp;Tao Tan","doi":"10.1016/j.compmedimag.2025.102497","DOIUrl":"10.1016/j.compmedimag.2025.102497","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduce the quality of MR images, and then affect the doctor’s diagnosis of diseases. Existing filtering methods, transform-domain methods, statistical methods and Convolutional Neural Network (CNN) methods main aim to denoise individual sequences of images without considering the relationships between multiple different sequences. They cannot balance the extraction of high-dimensional and low-dimensional features in MR images, and hard to maintain a good balance between preserving image texture details and denoising strength. To overcome these challenges, this work proposes a controllable Multimodal Cross-Global Learnable Attention Network (MMCGLANet) for MR image denoising with Arbitrary Modal Missing. Specifically, Encoder is employed to extract the shallow features of the image which share weight module, and Convolutional Long Short-Term Memory(ConvLSTM) is employed to extract the associated features between different frames within the same modal. Cross Global Learnable Attention Network(CGLANet) is employed to extract and fuse image features between multimodal and within the same modality. In addition, sequence code is employed to label missing modalities, which allows for Arbitrary Modal Missing during model training, validation, and testing. Experimental results demonstrate that our method has achieved good denoising results on different public and real MR image dataset.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"121 ","pages":"Article 102497"},"PeriodicalIF":5.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179162","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":"Feature-targeted deep learning framework for pulmonary tumorous Cone-beam CT (CBCT) enhancement with multi-task customized perceptual loss and feature-guided CycleGAN","authors":"Jiarui Zhu ,&nbsp;Hongfei Sun ,&nbsp;Weixing Chen ,&nbsp;Shaohua Zhi ,&nbsp;Chenyang Liu ,&nbsp;Mayang Zhao ,&nbsp;Yuanpeng Zhang ,&nbsp;Ta Zhou ,&nbsp;Yu Lap Lam ,&nbsp;Tao Peng ,&nbsp;Jing Qin ,&nbsp;Lina Zhao ,&nbsp;Jing Cai ,&nbsp;Ge Ren","doi":"10.1016/j.compmedimag.2024.102487","DOIUrl":"10.1016/j.compmedimag.2024.102487","url":null,"abstract":"<div><div>Thoracic Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for lung cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in loss of lung anatomy which contains crucial pulmonary tumorous and functional information. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details containing crucial tumorous information due to lack of targeted guidance. To address this issue, we propose a novel feature-targeted deep learning framework which generates ultra-quality pulmonary imaging from CBCT of lung cancer patients via a multi-task customized feature-to-feature perceptual loss function and a feature-guided CycleGAN. The framework comprises two main components: a multi-task learning feature-selection network (MTFS-Net) for building up a customized feature-to-feature perceptual loss function (CFP-loss); and a feature-guided CycleGan network. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9747 and an average PSNR index of 38.5995 globally, and an average Pearman’s coefficient of 0.8929 within the tumor region on multi-institutional datasets. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Functional imaging tests further demonstrated the pulmonary texture correction performance of the sCT images, and the similarity of the functional imaging generated from sCT and CT images has reached an average DSC value of 0.9147, SCC value of 0.9615 and R value of 0.9661. Comparison experiments with pixel-to-pixel loss also showed that the proposed perceptual loss significantly enhances the performance of involved generative models. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generating high-quality pulmonary imaging from CBCT that is suitable for supporting further analysis of lung cancer treatment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"121 ","pages":"Article 102487"},"PeriodicalIF":5.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive learning in brain imaging","authors":"Xiaoyin Xu ,&nbsp;Stephen T.C. Wong","doi":"10.1016/j.compmedimag.2025.102500","DOIUrl":"10.1016/j.compmedimag.2025.102500","url":null,"abstract":"<div><div>Contrastive learning is a type of deep learning technique trying to classify data or examples without requiring data labeling. Instead, it learns about the most representative features that contrast positive and negative pairs of examples. In literature of contrastive learning, terms of positive examples and negative examples do not mean whether the examples themselves are positive or negative of certain characteristics as one might encounter in medicine. Rather, positive examples just mean that the examples are of the same class, while negative examples mean that the examples are of different classes. Contrastive learning maps data to a latent space and works under the assumption that examples of the same class should be located close to each other in the latent space; and examples from different classes would locate far from each other. In other words, contrastive learning can be considered as a discriminator that tries to group examples of the same class together while separating examples of different classes from each other, preferably as far as possible. Since its inception, contrastive learning has been constantly evolving and can be realized as self-supervised, semi-supervised, or unsupervised learning. Contrastive learning has found wide applications in medical imaging and it is expected it will play an increasingly important role in medical image processing and analysis.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"121 ","pages":"Article 102500"},"PeriodicalIF":5.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076316","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":"Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT scans","authors":"Azka Rehman ,&nbsp;Jaewon Kim ,&nbsp;Lee Hyeokjong ,&nbsp;Jooyoung Chang ,&nbsp;Sang Min Park","doi":"10.1016/j.compmedimag.2025.102493","DOIUrl":"10.1016/j.compmedimag.2025.102493","url":null,"abstract":"<div><div>This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging, frequently used for diagnosing various conditions, contains opportunistic biomarkers that can be leveraged beyond their initial diagnostic purpose. Using a Cox proportional hazards-based survival loss, the DL-CVDi score captures complex, non-linear relationships between anatomical features and CVD risk. Clinical validation demonstrated that participants with high DL-CVDi scores had a significantly elevated risk of CVD incidents (hazard ratio [HR]: 2.75, 95% CI: 1.27–5.95, p-trend <span><math><mo>&lt;</mo></math></span>0.005) after adjusting for traditional risk factors. Additionally, the DL-CVDi score improved the concordance of baseline models, such as age and sex (from 0.662 to 0.700) and the Framingham Risk Score (from 0.697 to 0.742). Given its reliance on widely available abdominal CT data, the DL-CVDi score has substantial potential as an opportunistic screening tool for CVD risk in diverse clinical settings. Future research should validate these findings across multi-ethnic cohorts and explore its utility in patients with comorbid conditions, establishing the DL-CVDi score as a valuable addition to current CVD risk assessment strategies.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"120 ","pages":"Article 102493"},"PeriodicalIF":5.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043246","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":"A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1","authors":"Giulia Di Teodoro ,&nbsp;Federico Siciliano ,&nbsp;Valerio Guarrasi ,&nbsp;Anne-Mieke Vandamme ,&nbsp;Valeria Ghisetti ,&nbsp;Anders Sönnerborg ,&nbsp;Maurizio Zazzi ,&nbsp;Fabrizio Silvestri ,&nbsp;Laura Palagi","doi":"10.1016/j.compmedimag.2024.102484","DOIUrl":"10.1016/j.compmedimag.2024.102484","url":null,"abstract":"<div><div>Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion. Our model uses both tabular data about genetic sequences and a knowledge base derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence. By leveraging this knowledge base structured as a graph, the GNN component enables our model to adapt to imbalanced data distributions and account for Out-of-Distribution (OoD) drugs. We evaluated these models’ robustness against OoD drugs in the test set. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in contributing in more informed clinical decisions with limited data availability. The source code is available at <span><span>https://github.com/federicosiciliano/graph-ood-hiv</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"120 ","pages":"Article 102484"},"PeriodicalIF":5.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease","authors":"Yiwen Shen ,&nbsp;Li Chen ,&nbsp;Jieyi Liu ,&nbsp;Haobo Chen ,&nbsp;Changyan Wang ,&nbsp;Hong Ding ,&nbsp;Qi Zhang","doi":"10.1016/j.compmedimag.2024.102490","DOIUrl":"10.1016/j.compmedimag.2024.102490","url":null,"abstract":"<div><div>Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the <em>PA</em>rkinson disease <em>D</em>enoising and <em>S</em>egmentation <em>Net</em>work (PADS-Net), to simultaneously denoise and segment transcranial ultrasound images of midbrain for accurate PD diagnosis. The PADS-Net is built upon generative adversarial networks and incorporates a multi-task deep learning framework aimed at optimizing the tasks of denoising and segmentation for ultrasound images. A composite loss function including the mean absolute error, the mean squared error and the Dice loss, is adopted in the PADS-Net to effectively capture image details. The PADS-Net also integrates radiomics techniques for PD diagnosis by exploiting high-throughput features from ultrasound images. A four-branch ensemble diagnostic model is designed by utilizing two “wings” of the butterfly-shaped midbrain regions on both ipsilateral and contralateral images to enhance the accuracy of PD diagnosis. Experimental results demonstrate that the PADS-Net not only reduced speckle noise, achieving the edge-to-noise ratio of 16.90, but also attained a Dice coefficient of 0.91 for midbrain segmentation. The PADS-Net finally achieved an area under the receiver operating characteristic curve as high as 0.87 for diagnosis of PD. Our PADS-Net excels in transcranial ultrasound image denoising and segmentation and offers a potential clinical solution to accurate PD assessment.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"120 ","pages":"Article 102490"},"PeriodicalIF":5.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985555","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 Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging","authors":"Lidan Fu ,&nbsp;Lingbing Li ,&nbsp;Binchun Lu ,&nbsp;Xiaoyong Guo ,&nbsp;Xiaojing Shi ,&nbsp;Jie Tian ,&nbsp;Zhenhua Hu","doi":"10.1016/j.compmedimag.2025.102492","DOIUrl":"10.1016/j.compmedimag.2025.102492","url":null,"abstract":"<div><div>In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information. In this work, we introduce an end-to-end model-driven Deep Equilibrium Unfolding Mamba (DEQ-UMamba) that integrates proximal gradient descent technique and learnt spatial-frequency characteristics to decouple complex noise structures into statistical distributions, enabling effective noise estimation and suppression in fluorescent images. Moreover, to address the computational limitations of unfolding networks, DEQ-UMamba trains an implicit mapping by directly differentiating the equilibrium point of the convergent solution, thereby ensuring stability and avoiding non-convergent behavior. With each network module aligned to a corresponding operation in the iterative optimization process, the proposed method achieves clear structural interpretability and strong performance. Comprehensive experiments conducted on both clinical and in vivo datasets demonstrate that DEQ-UMamba outperforms current state-of-the-art alternatives while utilizing fewer parameters, facilitating the advancement of cost-effective and high-quality clinical molecular imaging.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"120 ","pages":"Article 102492"},"PeriodicalIF":5.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015674","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":"NURBS curve shape prior-guided multiscale attention network for automatic segmentation of the inferior alveolar nerve","authors":"Shuanglin Jiang ,&nbsp;Jiangchang Xu ,&nbsp;Wenyin Wang ,&nbsp;Baoxin Tao ,&nbsp;Yiqun Wu ,&nbsp;Xiaojun Chen","doi":"10.1016/j.compmedimag.2024.102485","DOIUrl":"10.1016/j.compmedimag.2024.102485","url":null,"abstract":"<div><div>Accurate segmentation of the inferior alveolar nerve (IAN) within Cone-Beam Computed Tomography (CBCT) images is critical for the precise planning of oral and maxillofacial surgeries, especially to avoid IAN damage. Existing methods often fail due to the low contrast of the IAN and the presence of artifacts, which can cause segmentation discontinuities. To address these challenges, this paper proposes a novel approach that employs Non-Uniform Rational B-Spline (NURBS) curve shape priors into a multiscale attention network for the automatic segmentation of the IAN. Firstly, an automatic method for generating non-uniform rational B-spline (NURBS) shape prior is proposed and introduced into the segmentation network, which significantly enhancing the continuity and accuracy of IAN segmentation. Then a multiscale attention segmentation network, incorporating a dilation selective attention module is developed, to improve the network’s feature extraction capacity. The proposed approach is validated on both in-house and public datasets, showcasing superior performance compared to established benchmarks, achieving 80.29±11.04% dice coefficient (Dice) and 68.14±12.06% intersection of union (IoU), the 95% Hausdorff distance (95HD) reaches 1.61±6.14 mm and mean surface distance (MSD) reaches 0.64±2.16 mm on private dataset. On public dataset, the Dice reaches 80.69±4.93%, IoU reaches 67.86±6.73%, 95HD reaches 1.04±0.95 mm, and MSD reaches 0.42±0.34 mm. Compared to state-of-the-art networks, the proposed approach out-performs in both voxel accuracy and surface distance. It offers significant potential to improve doctors’ efficiency in segmentation tasks and holds promise for applications in dental surgery planning. The source codes are available at <span><span>https://github.com/SJTUjsl/NURBS_IAN.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"120 ","pages":"Article 102485"},"PeriodicalIF":5.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967409","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}