Medical & Biological Engineering & Computing最新文献_第9页

A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT. 随机多窗口三维深度学习方法在非对比头部CT颅内出血检测中的应用。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-07 DOI: 10.1007/s11517-026-03620-1

Gül Cihan Habek, Fatih Basciftci

{"title":"A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.","authors":"Gül Cihan Habek, Fatih Basciftci","doi":"10.1007/s11517-026-03620-1","DOIUrl":"10.1007/s11517-026-03620-1","url":null,"abstract":"Intracranial hemorrhage (ICH) is an emergency clinical condition that requires rapid diagnosis. In patients with suspected ICH, head computed tomography (CT) is the first-line imaging modality because it provides fast results. In recent years, interest has increased in 3D deep learning approaches for automated scan-level ICH detection. However, real-world deployability is limited by high computational cost, memory requirements, and inference latency. In this study, we present Lite3DNet, a lightweight architecture that uses randomized multi-window 3D input. We used the Radiological Society of North America (RSNA) ICH Detection dataset, the largest public dataset related to ICH. DICOM series were converted into 3D volumes, data cleaning steps were applied, and single-window, three-window, and training-time window jittering strategies were investigated. The proposed architecture was trained using 5-fold cross-validation and evaluated on a held-out internal test set and two external test sets, CQ500 and MosMedData CT Hemorrhage (MosMed). The results show that Lite3DNet achieves high discriminative performance. In ensemble evaluation, the model reached an AUC of 0.986 on the RSNA ICH Detection held-out internal test set, 0.956 on the external CQ500 test set and 0.967 on the external MosMed test set. With 1.189 million parameters and 19.84 ms GPU inference latency, the model delivers low computational cost while remaining suitable for clinical triage applications that require rapid inference.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3053-3073"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study of cross-dataset generalization on liver vessel segmentation improved by a topological loss. 基于拓扑损失的肝血管分割的跨数据集泛化研究。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-21 DOI: 10.1007/s11517-026-03636-7

Jose M Saavedra, Héctor Henríquez, Miguel Chicchon, Camila Figueroa, Marcelo Pizarro, Joaquín Curimil, Violeta Chang

引用次数: 0

Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework. 利用多模态定量超声和人工智能框架对肝纤维化、炎症和脂肪变性进行同步分级诊断。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2024-07-11 DOI: 10.1007/s11517-024-03159-z

Xingyue Wei, Yuanyuan Wang, Lianshuang Wang, Mengze Gao, Qiong He, Yao Zhang, Jianwen Luo

{"title":"Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework.","authors":"Xingyue Wei, Yuanyuan Wang, Lianshuang Wang, Mengze Gao, Qiong He, Yao Zhang, Jianwen Luo","doi":"10.1007/s11517-024-03159-z","DOIUrl":"10.1007/s11517-024-03159-z","url":null,"abstract":"Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and improving the prognosis quality of chronic liver diseases (CLDs). In this study, we established an artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems, using the liver biopsy results as the classification criteria. One hundred forty-two patients diagnosed with CLD were enrolled in this study. The results show that for the classification of fibrosis grade ≥ F1, ≥ F2, ≥ F3, and F4, the highest AUCs were respectively 0.69, 0.82, 0.84, and 0.88 with single clinical indicator alone, and were 0.81, 0.83, 0.89, and 0.91 with the proposed method. For the classification of inflammation grade ≥ A2 and A3, the highest AUCs were respectively 0.66 and 0.76 with single clinical indicator alone and were 0.80 and 0.93 with the proposed method. For the classification of steatosis grade ≥ S1 and ≥ S2, the highest AUCs were respectively 0.71 and 0.90 with single clinical indicator alone and were 0.75 and 0.92 with the proposed method. The proposed method can effectively improve the grading diagnosis performance compared with the present clinical indicators and has potential applications for noninvasive, accurate, and simultaneous diagnosis of CLDs.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2875-2887"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robot-assisted laser osteotomy system based on dose-effect model-guided feedforward control. 基于剂量效应模型导向前馈控制的机器人辅助激光截骨系统。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-07 DOI: 10.1007/s11517-026-03614-z

Zeng Kuang, Yanping Lin, Shiqi Peng, Yinwei Li, Huifang Zhou

{"title":"Robot-assisted laser osteotomy system based on dose-effect model-guided feedforward control.","authors":"Zeng Kuang, Yanping Lin, Shiqi Peng, Yinwei Li, Huifang Zhou","doi":"10.1007/s11517-026-03614-z","DOIUrl":"10.1007/s11517-026-03614-z","url":null,"abstract":"This study aimed to develop and evaluate a robot-assisted laser osteotomy system that regulates both ablation trajectory and ablation depth through dose-effect model-guided feedforward planning. A Ho:YAG laser delivery and scanning module was integrated with a multi-degree-of-freedom robotic platform, optical navigation, and layered osteotomy planning. Ex vivo porcine femur experiments were performed to establish tissue-specific dose-effect relationship models for cortical and cancellous bone under dynamic scanning conditions. The established models were incorporated into the planning layer to determine the number of ablation layers, scan passes, laser parameters, and robotic scanning trajectories before execution. The system was validated on ex vivo porcine mandible specimens using multiple curved osteotomy paths designed from preoperative CT-based three-dimensional models. Quantitative comparison between the planned and executed ablation geometries showed a mean positional error of 0.85 ± 0.50 mm, a mean angular error of 2.96[Formula: see text] ± 1.85[Formula: see text], and a mean ablation depth error of 0.51 ± 0.50 mm. These results indicate that the proposed feedforward planning strategy can provide predictable trajectory execution and ablation-depth regulation without requiring continuous real-time depth feedback during execution. The proposed system provides an engineering basis for sensing-constrained robot-assisted laser osteotomy, while further validation is required to improve model generalizability and assess its clinical applicability in more complex surgical scenarios.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3035-3052"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying the development of visual expertise in medical image interpretation using fractal eye-gaze metrics. 使用分形人眼注视度量来量化医学图像解释中视觉专业知识的发展。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-09 DOI: 10.1007/s11517-026-03613-0

Ghasem Azemi, Poonam Kumari, Carlo Russo, Antonio Di Ieva

{"title":"Quantifying the development of visual expertise in medical image interpretation using fractal eye-gaze metrics.","authors":"Ghasem Azemi, Poonam Kumari, Carlo Russo, Antonio Di Ieva","doi":"10.1007/s11517-026-03613-0","DOIUrl":"10.1007/s11517-026-03613-0","url":null,"abstract":"This study presents a fractal-based framework for quantifying visual expertise using eye-gaze data. A longitudinal experiment was conducted across three consecutive semesters of the medical program at Macquarie University, where the eye movements of 13 medical students were recorded with a high-resolution eye tracker while they viewed pathological images from multiple imaging modalities. In addition, eye-tracking data from 13 consultant neurosurgeons were included as expert reference data to assess whether longitudinal changes in students' approach mirror expert-like visual behavior. Three metrics were computed: the two- and three-dimensional fractal dimensions of eye-gaze to capture spatiotemporal complexity of the eye-gaze data, and a fractal-dimension-based correlation to quantify the relationship between gaze patterns and stimulus structure. These metrics were combined into a composite Fractal Eye-Gaze Expertise Index (FEI). The results show systematic reductions in fractal complexity across sessions, indicating increasingly structured and efficient gaze strategies, with trends approaching those of experts. Crucially, conventional fixation-duration-based measures failed to consistently capture this progression, unlike the proposed fractal metrics. Statistical analyses confirm significant session-related effects for all metrics, and receiver operating characteristic curve (ROC) analysis shows that the FEI effectively discriminates longitudinal differences in visual expertise. This work demonstrates that fractal-based eye-gaze modelling provides an objective and scalable approach to characterizing visual expertise.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3075-3088"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images. 基于深度学习的三维超声图像中腹主动脉瘤和腔内血栓的分割。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2024-10-25 DOI: 10.1007/s11517-024-03216-7

Arjet Nievergeld, Bünyamin Çetinkaya, Esther Maas, Marc van Sambeek, Richard Lopata, Navchetan Awasthi

{"title":"Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images.","authors":"Arjet Nievergeld, Bünyamin Çetinkaya, Esther Maas, Marc van Sambeek, Richard Lopata, Navchetan Awasthi","doi":"10.1007/s11517-024-03216-7","DOIUrl":"10.1007/s11517-024-03216-7","url":null,"abstract":"Ultrasound (US)-based patient-specific rupture risk analysis of abdominal aortic aneurysms (AAAs) has shown promising results. Input for these models is the patient-specific geometry of the AAA. However, segmentation of the intraluminal thrombus (ILT) remains challenging in US images due to the low ILT-blood contrast. This study aims to improve AAA and ILT segmentation in time-resolved three-dimensional (3D + t) US images using a deep learning approach. In this study a \"no new net\" (nnU-Net) model was trained on 3D + t US data using either US-based or (co-registered) computed tomography (CT)-based annotations. The optimal training strategy for this low-contrast data was determined for a limited dataset. The merit of augmentation was investigated, as well as the inclusion of low-contrast areas. Segmentation results were validated with CT-based geometries as the ground truth. The model trained on CT-based masks showed the best performance in terms of DICE index, Hausdorff distance, and diameter differences, covering a larger part of the AAA. With a higher accuracy and less manual input the model outperforms conventional methods, with a mean Hausdorff distance of 4.4 mm for the vessel and 7.8 mm for the lumen. However, visibility of the lumen-ILT interface remains the limiting factor, necessitating improvements in image acquisition to ensure broader patient inclusion and enable rupture risk assessment of AAAs in the future.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2903-2916"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI4US: Revolutionizing ultrasound image processing and analysis with artificial intelligence. AI4US：用人工智能革新超声图像处理和分析。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 DOI: 10.1007/s11517-026-03667-0

Sara Moccia, Maria Chiara Fiorentino, Selene Tomassini

引用次数: 0

Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images? 将图像分辨率纳入神经网络能否提高超声图像中肾肿瘤的分类性能？

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2024-08-31 DOI: 10.1007/s11517-024-03188-8

Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang

{"title":"Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?","authors":"Haihao He, Yuhan Liu, Xin Zhou, Jia Zhan, Changyan Wang, Yiwen Shen, Haobo Chen, Lin Chen, Qi Zhang","doi":"10.1007/s11517-024-03188-8","DOIUrl":"10.1007/s11517-024-03188-8","url":null,"abstract":"Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2889-2901"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery. 微创脊柱手术的三维超声形状完成及解剖特征检测。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-04-22 DOI: 10.1007/s11517-025-03359-1

Ruixuan Li, Yuyu Cai, Ayoob Davoodi, Gianni Borghesan, Emmanuel Vander Poorten

{"title":"3D ultrasound shape completion and anatomical feature detection for minimally invasive spine surgery.","authors":"Ruixuan Li, Yuyu Cai, Ayoob Davoodi, Gianni Borghesan, Emmanuel Vander Poorten","doi":"10.1007/s11517-025-03359-1","DOIUrl":"10.1007/s11517-025-03359-1","url":null,"abstract":"Ultrasound (US) with 3D reconstruction is being explored to offer a radiation-free approach to visualizing anatomical structures. Such a method could be useful for navigating and assisting minimally invasive spine surgery where direct sight on the surgical site is absent. During surgery, the pre-operative CT model and surgical plans are registered to the patient's anatomy by using intra-operative US reconstruction. However, accurate and automatic registration remains challenging. This difficulty arises from an incomplete detection of the bone geometry in US images and the challenges in identifying anatomical landmarks. To address the problem, this work presents a pipeline to automate the workflow by offering an initial CT-to-US registration. This work utilizes PointAttN for 3D shape completion that completes occluded bone structures from partial US reconstruction. This enriched point cloud is then segmented using PointNet++ to identify specific anatomical features. To train the network, synthetic 3D representations of partial views are generated from fifty CT models of the lumbar spine by simulating US physics, effectively mimicking the intraoperative scenario. The proposed work yields a mean registration error of 1.34 mm and 1.63 mm on real US reconstructions of agar phantoms and an ex vivo human spine, respectively. This comprehensive 3D representation enhances anatomical feature interpretation, enabling robust automatic registration. The clinical potential of this framework merits further investigation in pre-clinical trials.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3005-3018"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal graph learning for chagas disease classification. 恰加斯病分类的多模态图学习。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-17 DOI: 10.1007/s11517-026-03631-y

Gabriel Carcedo-Rodríguez, Erik Molino-Minero-Re, Jorge Perez-Gonzalez, Nidiyare Hevia-Montiel

{"title":"Multimodal graph learning for chagas disease classification.","authors":"Gabriel Carcedo-Rodríguez, Erik Molino-Minero-Re, Jorge Perez-Gonzalez, Nidiyare Hevia-Montiel","doi":"10.1007/s11517-026-03631-y","DOIUrl":"10.1007/s11517-026-03631-y","url":null,"abstract":"The accurate classification of Chagas disease stages based on Trypanosoma cruzi infection in experimental models faces challenges due to the scarcity of data and the high dimensionality of diagnostic sources. This study proposes an architecture based on Graph Attention Networks on fully connected graphs to combine multimodal features (Electrocardiogram, Echocardiogram, Doppler, and ELISA). A Variational Graph Autoencoder was used to implement a data augmentation strategy that reduces overfitting and optimizes generalization, producing synthetic subjects with a biological covariance structure. The results demonstrate that the proposed methodology is effective compared to state-of-the-art classifiers, achieving an AUROC of 100% in the identification of infection stage through multimodal fusion and significantly optimizing performance in complex modalities such as Doppler. The pathophysiological consistency of the model's decisions was also validated using an interpretability scheme (GNNExplainer), which allowed the detection of important clinical features and established GATs as a robust method in the classification of Trypanosoma cruzi infection stages.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3181-3198"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0