Medical & Biological Engineering & Computing最新文献_第8页

M4S-Net: a motion-enhanced shape-aware semi-supervised network for echocardiography sequence segmentation. M4S-Net：用于超声心动图序列分割的运动增强形状感知半监督网络。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-02-24 DOI: 10.1007/s11517-025-03330-0

Mingshan Li, Fangyan Tian, Shuyu Liang, Qin Wang, Xianhong Shu, Yi Guo, Yuanyuan Wang

{"title":"M4S-Net: a motion-enhanced shape-aware semi-supervised network for echocardiography sequence segmentation.","authors":"Mingshan Li, Fangyan Tian, Shuyu Liang, Qin Wang, Xianhong Shu, Yi Guo, Yuanyuan Wang","doi":"10.1007/s11517-025-03330-0","DOIUrl":"10.1007/s11517-025-03330-0","url":null,"abstract":"Sequence segmentation of echocardiograms is of great significance for the diagnosis and treatment of cardiovascular diseases. However, the low quality of ultrasound imaging and the complexity of cardiac motion pose great challenges to it. In addition, the difficulty and cost of labeling echocardiography sequences limit the performance of supervised learning methods. In this paper, we proposed a Motion-enhanced Shape-aware Semi-supervised Sequence Segmentation Network named M4S-Net. First, multi-level shape priors are used to enhance the model's shape representation capabilities, overcoming the low image quality and improving single-frame segmentation. Then, a motion-enhanced optimization module utilizes optical flows to assist segmentation in a geometric sense, which robustly responds to the complex motions and ensures the temporal consistency of sequence segmentation. A hybrid loss function is devised to maximize the effectiveness of each module and further improve the temporal stability of predicted masks. Furthermore, the parameter-sharing strategy allows it to perform sequence segmentation in a semi-supervised manner. Massive experiments on both public and in-house datasets show that M4S-Net outperforms the state-of-the-art methods in both spatial and temporal segmentation performance. A downstream apical rocking recognition task based on M4S-Net also achieves an AUC of 0.944, which significantly exceeds specialized physicians.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2949-2965"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494472","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 coarse-to-fine machine-learning framework for identifying functional connectivity markers of cognitive impairment in Parkinson's disease. 一个从粗到精的机器学习框架，用于识别帕金森病认知障碍的功能连接标记。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-11 DOI: 10.1007/s11517-026-03619-8

Chung-Yao Chien, Tsung-Lin Lee, Tien-Yu Lin, Rwei-Ling Yu, Chou-Ching K Lin

{"title":"A coarse-to-fine machine-learning framework for identifying functional connectivity markers of cognitive impairment in Parkinson's disease.","authors":"Chung-Yao Chien, Tsung-Lin Lee, Tien-Yu Lin, Rwei-Ling Yu, Chou-Ching K Lin","doi":"10.1007/s11517-026-03619-8","DOIUrl":"10.1007/s11517-026-03619-8","url":null,"abstract":"Background: Resting-state functional MRI (rs-fMRI) has been applied to investigate cognitive impairment (CI) in Parkinson's disease (PD). Nevertheless, reported functional connectivity (FC) alterations remain heterogeneous, partly due to reliance on linear analytical approaches and limited validation across datasets.Objective: To develop a machine-learning framework for identifying generalizable FC markers of CI in PD.Methods: Rs-fMRI data were obtained from an online cohort (for model training) and an independent local cohort (for external validation) of individuals with PD. Subjects were stratified according to the presence of CI. All images were preprocessed using an identical pipeline to derive whole-brain FC. A coarse-to-fine feature selection strategy was implemented, combining a genetic algorithm for global feature reduction with sequential feature selection using leave-one-out cross-validation.Results: In the training dataset (n = 181), genetic algorithm-based selection reduced 13,366 ROI-pair features to 229, achieving an accuracy of 0.83. Subsequent sequential selection further reduced the feature set to 10 ROI pairs, improving accuracy to 0.92. In the validation dataset (n = 32), the classification accuracy was 0.88, with FC patterns showing lateralized cortical and cerebellar involvement.Conclusion: The proposed framework identifies interpretable signatures of rs-fMRI-based markers associated with CI in PD and demonstrates the generalizability.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3089-3099"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425945","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

Nonhomogeneous smoke mitigation in laparoscopic images using a hybrid physical-neural model. 使用混合物理-神经模型的腹腔镜图像非均匀烟雾缓解。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-14 DOI: 10.1007/s11517-026-03628-7

Victor Diaz-Ramirez, Jose Godoy, Christian Gaxiola, Rigoberto Juarez-Salazar, Leonardo Trujillo

{"title":"Nonhomogeneous smoke mitigation in laparoscopic images using a hybrid physical-neural model.","authors":"Victor Diaz-Ramirez, Jose Godoy, Christian Gaxiola, Rigoberto Juarez-Salazar, Leonardo Trujillo","doi":"10.1007/s11517-026-03628-7","DOIUrl":"10.1007/s11517-026-03628-7","url":null,"abstract":"Surgical smoke generated during electrocautery in minimally invasive surgery introduces nonhomogeneous optical scattering that compromises visibility and clinical interpretability of laparoscopic images. This work proposes a hybrid physics- and learning-based method for surgical smoke mitigation combining a convolutional neural network-vision transformer (CNN-ViT) architecture, which jointly estimates the spatially varying transmission map and ambient illumination, with a physics-based restoration model derived from atmospheric optics. Training data are generated through a Navier-Stokes fluid dynamics simulation of surgical smoke, producing physically grounded, nonhomogeneous degradation patterns representative of intraoperative conditions. The method was evaluated on different unseen laparoscopic sequences and compared against four state-of-the-art baselines, consistently outperforming all methods in both PSNR and SSIM. Evaluation on real electrocautery smoke demonstrates effective generalization to clinically realistic conditions, recovering anatomical structures and tissue detail without reliance on synthetic smoke degradation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3141-3160"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438718","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

Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach. 经食管超声心动图自动四维二尖瓣分割：半监督学习方法。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-01-11 DOI: 10.1007/s11517-024-03275-w

Riccardo Munafò, Simone Saitta, Davide Tondi, Giacomo Ingallina, Paolo Denti, Francesco Maisano, Eustachio Agricola, Emiliano Votta

{"title":"Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.","authors":"Riccardo Munafò, Simone Saitta, Davide Tondi, Giacomo Ingallina, Paolo Denti, Francesco Maisano, Eustachio Agricola, Emiliano Votta","doi":"10.1007/s11517-024-03275-w","DOIUrl":"10.1007/s11517-024-03275-w","url":null,"abstract":"Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used. The Teacher model, an ensemble of three convolutional neural networks, is trained on end-systole and end-diastole frames and is used to generate MV pseudo-segmentations on intermediate frames of the cardiac cycle. The pseudo-annotated frames augment the Student model's training set, improving segmentation accuracy and temporal consistency. The Student outperforms individual Teachers, achieving a Dice score of 0.82, an average surface distance of 0.37 mm, and a 95% Hausdorff distance of 1.72 mm for MV leaflets. The Student model demonstrates reliable frame-by-frame MV segmentation, accurately capturing leaflet morphology and dynamics throughout the cardiac cycle, with a significant reduction in inference time compared to the ensemble. This approach greatly reduces manual annotation workload and ensures reliable, repeatable, and time-efficient MV analysis. Our method holds strong potential to enhance the precision and efficiency of MV diagnostics and treatment planning in clinical settings.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2933-2948"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967338","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

Individualized multimodal integration of optimized dual fluoroscopic imaging registration and finite element modeling for knee joint stress during stair ascent. 个性化的多模式集成优化双透视成像配准和有限元建模膝关节应力在楼梯上升。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1007/s11517-026-03615-y

Qiaolin Zhang, Yining Xu, Dong Sun, Hairong Chen, József Sárosi, István Bíró, Yaodong Gu

{"title":"Individualized multimodal integration of optimized dual fluoroscopic imaging registration and finite element modeling for knee joint stress during stair ascent.","authors":"Qiaolin Zhang, Yining Xu, Dong Sun, Hairong Chen, József Sárosi, István Bíró, Yaodong Gu","doi":"10.1007/s11517-026-03615-y","DOIUrl":"10.1007/s11517-026-03615-y","url":null,"abstract":"Stair ascent imposes high mechanical demands on the knee, but the effect of stair height on internal joint stress remains unclear. This study developed a subject-specific workflow to quantify meniscal and cruciate ligament stresses during stair ascent in a female. One female ascended stair with five stair heights (120-200 mm). Dual fluoroscopic imaging system (DFIS), CT, motion capture, force plate, and EMG were integrated to obtain in vivo knee kinematics and muscle forces via OpenSim. These data drove a quasi-dynamic finite element model of the tibiofemoral joint to compute region-specific meniscal and anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) stresses at key stance phases. Meniscal stress increased with stair height and concentrated in the medial meniscus body between 25 and 50% of the support phase. PCL stress peaked early in stance and rose markedly with stair height, whereas ACL stress peaked at mid-stance and showed a more moderate height-dependent increase. This individualized multimodal framework enables realistic assessment of knee loading during stair ascent and identifies stair height as a key factor influencing meniscal and cruciate ligament stress in adults.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3127-3140"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431390","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

Thorax shape reconstruction from limited CT-digitized palpable landmarks using statistical shape modeling. 利用统计形状建模从有限的ct数字化可触及地标重建胸腔形状。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2026-07-11 DOI: 10.1007/s11517-026-03624-x

Ricardo Alcobia, João Folgado, Carlos Quental

{"title":"Thorax shape reconstruction from limited CT-digitized palpable landmarks using statistical shape modeling.","authors":"Ricardo Alcobia, João Folgado, Carlos Quental","doi":"10.1007/s11517-026-03624-x","DOIUrl":"10.1007/s11517-026-03624-x","url":null,"abstract":"This study aimed to develop and evaluate algorithms for reconstructing subject-specific thorax geometries from a limited set of skin landmarks, typically collected in motion analysis studies, using statistical shape models (SSMs). A dataset of DICOM CT scans from 76 individuals was used to generate thorax geometries, comprising the sternum, ribs, and vertebrae C6 to T10, and to collect five bone landmarks and their corresponding skin landmarks. Two thorax SSMs were developed from the CT-derived bone geometries, differing in whether skin landmarks were embedded within the SSM. Based on these models, two reconstruction strategies were implemented using an optimization framework: (i) a bone-landmark-based approach in which multiple linear regression was used to map skin landmarks to bone landmarks (SSM-BL), which served as optimization targets, and (ii) a skin-landmark-embedded approach that used skin landmarks directly as optimization targets (SSM-SL). Skin-to-bone mapping accuracy was quantified using mean absolute error (MAE) and root-mean-square error (RMSE). Thorax reconstruction accuracy was assessed using point-to-point surface errors, reported using MAE and RMSE. Skin-to-bone regression models achieved MAE and RMSE below 5 mm for most landmark coordinates. No statistically significant differences were observed between reconstruction strategies. Thorax reconstruction errors ranged from 8.26 ± 1.96 mm to 8.36 ± 1.70 mm (MAE) and from 9.10 ± 1.12 mm to 9.32 ± 1.49 mm (RMSE), depending on the number of retained principal components. The proposed SSM-based methods enable thorax reconstruction from sparse data, capturing global thorax morphology. However, local anatomical details were less accurately represented.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3115-3126"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425465","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

Ultrasound detection of nonalcoholic steatohepatitis using convolutional neural networks with dual-branch global-local feature fusion architecture. 基于双分支全局-局部特征融合结构的卷积神经网络超声检测非酒精性脂肪性肝炎。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-04-21 DOI: 10.1007/s11517-025-03361-7

Trina Chattopadhyay, Chun-Hao Lu, Yi-Ping Chao, Chiao-Yin Wang, Dar-In Tai, Ming-Wei Lai, Zhuhuang Zhou, Po-Hsiang Tsui

{"title":"Ultrasound detection of nonalcoholic steatohepatitis using convolutional neural networks with dual-branch global-local feature fusion architecture.","authors":"Trina Chattopadhyay, Chun-Hao Lu, Yi-Ping Chao, Chiao-Yin Wang, Dar-In Tai, Ming-Wei Lai, Zhuhuang Zhou, Po-Hsiang Tsui","doi":"10.1007/s11517-025-03361-7","DOIUrl":"10.1007/s11517-025-03361-7","url":null,"abstract":"Nonalcoholic steatohepatitis (NASH) is a contributing factor to liver cancer, with ultrasound B-mode imaging as the first-line diagnostic tool. This study applied deep learning to ultrasound B-scan images for NASH detection and introduced an ultrasound-specific data augmentation (USDA) technique with a dual-branch global-local feature fusion architecture (DG-LFFA) to improve model performance and adaptability across imaging conditions. A total of 137 participants were included. Ultrasound images underwent data augmentation (rotation and USDA) for training and testing convolutional neural networks-AlexNet, Inception V3, VGG16, VGG19, ResNet50, and DenseNet201. Gradient-weighted class activation mapping (Grad-CAM) analyzed model attention patterns, guiding the selection of the optimal backbone for DG-LFFA implementation. The models achieved testing accuracies of 0.81-0.83 with rotation-based data augmentation. Grad-CAM analysis showed that ResNet50 and DenseNet201 exhibited stronger liver attention. When USDA simulated datasets from different imaging conditions, DG-LFFA (based on ResNet50 and DenseNet201) improved accuracy (0.79 to 0.84 and 0.78 to 0.83), recall (0.72 to 0.81 and 0.70 to 0.78), and F1 score (0.80 to 0.84 for both models). In conclusion, deep architectures (ResNet50 and DenseNet201) enable focused analysis of liver regions for NASH detection. Under USDA-simulated imaging variations, the proposed DG-LFFA framework further improves diagnostic performance.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2989-3003"},"PeriodicalIF":3.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036836","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

Editorial: AI4US Special Issue. 社论：AI4US特刊。

IF 3.1 4区医学

Medical & Biological Engineering & Computing Pub Date : 2026-08-01 Epub Date: 2025-12-05 DOI: 10.1007/s11517-025-03485-w

Maria Chiara Fiorentino, Selene Tomassini, Sara Moccia

引用次数: 0

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