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

Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review. 非侵入性脑机接口在运动或交流辅助和康复中的范例和方法：系统综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-10 DOI: 10.1007/s11517-025-03340-y

Jianjun Meng, Yuxuan Wei, Ximing Mai, Songwei Li, Xu Wang, Ruijie Luo, Minghao Ji, Xiangyang Zhu

{"title":"Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.","authors":"Jianjun Meng, Yuxuan Wei, Ximing Mai, Songwei Li, Xu Wang, Ruijie Luo, Minghao Ji, Xiangyang Zhu","doi":"10.1007/s11517-025-03340-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03340-y","url":null,"abstract":"Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587916","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

Strain energy in human tibia during different exercises with adjustable leg weights: a subject-specific computational model analysis. 应变能在人类胫骨在不同的运动与可调的腿部重量：一个主题特定的计算模型分析。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-07 DOI: 10.1007/s11517-025-03335-9

Xuan Guo, XinSheng Xu, Xiang Geng, Zhenming Zhang, Xin Ma, Wen-Ming Chen

{"title":"Strain energy in human tibia during different exercises with adjustable leg weights: a subject-specific computational model analysis.","authors":"Xuan Guo, XinSheng Xu, Xiang Geng, Zhenming Zhang, Xin Ma, Wen-Ming Chen","doi":"10.1007/s11517-025-03335-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03335-9","url":null,"abstract":"Physical exercise is recommended to improve tibia strength, a common site for stress injuries, while identifying optimal training regimens remains a significant challenge. This study investigated tibial responses to varied exercise regimens using a subject-specific computational modeling approach. A subject-specific neuro-musculoskeletal model was combined with a finite element model to assess the effects of various exercises (jumping, landing, squatting, and walking) on tibial strain energy density (SED), as well as the impact of adjustable leg weights placed at different sites (shank versus thigh). The temporal relationship between joint/muscular loads and SED was then analyzed. A non-linear relationship between load weights and SED increase was observed, with 4% body weight load being the optimal load weight. Additionally, load carriage sites significantly influenced SED levels, emphasizing the necessity for individualized training regimens. The gastrocnemius, soleus, and peroneal muscles were identified as key contributors to tibial SED, with the highest correlations observed during various activities. This study underscored the utility of the subject-specific computational model in assessing the biomechanical impact of varied load weights, load sites, and exercise types. For a target bone site, it is beneficial to customize exercise programs based on individual biomechanical properties in order to maximize training benefits and meanwhile reduce risks of injuries.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574483","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

Hybrid model of feature-driven modular neural network-based grasshopper optimization algorithm for diabetic retinopathy classification using fundus images. 基于特征驱动模块化神经网络的混合算法用于眼底图像的糖尿病视网膜病变分类。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-06 DOI: 10.1007/s11517-025-03307-z

D Binny Jeba Durai, T Jaya

{"title":"Hybrid model of feature-driven modular neural network-based grasshopper optimization algorithm for diabetic retinopathy classification using fundus images.","authors":"D Binny Jeba Durai, T Jaya","doi":"10.1007/s11517-025-03307-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03307-z","url":null,"abstract":"Diabetic retinopathy (DR) is a progressive condition that can lead to blindness if undiagnosed or untreated. Automatic systems for DR prediction using fundus images have been developed, but challenges like variable illumination, overfitting, small datasets, poor feature learning, high computational complexity, and suboptimal feature weighting persist. To address these, a hybrid model called the modular neural network with grasshopper optimization algorithm (MNN-GOA) is proposed. This model integrates neural network capabilities with the grasshopper optimization algorithm (GOA) to enhance feature selection and classification accuracy. It begins with preprocessing to improve image quality, followed by data augmentation and histogram-based segmentation to focus on critical regions. Features are extracted using techniques like histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), color features, and mutual information (MI). GOA optimizes feature weights, balancing exploration and exploitation, while reducing computational complexity. The model integrates features from ground truth and original images to predict DR stages accurately. Achieving performance metrics of accuracy (98.8%), specificity (97.6%), sensitivity (96.8%), precision (96.4%), and F1 score (96.2%), the MNN-GOA model was validated on four datasets like DIARETDB1, DDR, APTOS 2019, and EyePACS and outperformed existing methods, proving to be a robust and efficient solution for DR classification and severity prediction.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568598","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

OCCMNet: Occlusion-Aware Class Characteristic Mining Network for multi-class artifacts detection in endoscopy. OCCMNet：用于内窥镜中多类伪影检测的闭塞感知类特征挖掘网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-05 DOI: 10.1007/s11517-025-03332-y

Chenchu Xu, Yu Chen, Jie Liu, Boyan Wang, Yanping Zhang, Jie Chen, Shu Zhao

{"title":"OCCMNet: Occlusion-Aware Class Characteristic Mining Network for multi-class artifacts detection in endoscopy.","authors":"Chenchu Xu, Yu Chen, Jie Liu, Boyan Wang, Yanping Zhang, Jie Chen, Shu Zhao","doi":"10.1007/s11517-025-03332-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03332-y","url":null,"abstract":"Multi-class endoscope artifacts detection is crucial for eliminating interference caused by artifacts during clinical examinations and reducing the rate of misdiagnosis and missed diagnoses by physicians. However, this task remains challenging such as data imbalance, similarity, and occlusion among artifacts. To overcome these challenges, we propose an Occlusion-Aware Class Characteristic Mining Network (OCCMNet) to detect eight classes of artifacts in endoscope simultaneously. The OCCMNet comprises the following: (1) A Dual-Branch Class Rebalancing Module (DCRM) rebalances the impact of various classes by fully exploiting the benefits of two complementary data distributions, sampling and detecting from the majority and minority classes respectively. (2) A Class Discrimination Enhancement Module (CDEM) effectively enhances the discrepancy of inter-class by enhance important information and introduce nuance information nonlinearly. (3) A Global Occlusion-Aware Module (GOAM) infers the obscured part of the artifacts by capturing the global information to initially identify the obscured artifacts and combining local details to sense the overall structure of the artifacts. Our OCCMNet has been validated on a public dataset (EndoCV2020). Compared to the latest methods in both medical and computer vision detection, our approach demonstrated 3.5-6.5% improvement in mAP50. The results proved the superiority of our OCCMNet in multi-class endoscopic artifact detection and demonstrated its great potential in reducing clinical interference.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558461","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

Comprehensive comparison of different BITA graft configurations: a computational study integrating TTFM and hemodynamic predictors. 综合比较不同的BITA移植物构型：一项整合TTFM和血流动力学预测因子的计算研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-05 DOI: 10.1007/s11517-025-03336-8

Ahmad Masoudi, Hossein Ali Pakravan

{"title":"Comprehensive comparison of different BITA graft configurations: a computational study integrating TTFM and hemodynamic predictors.","authors":"Ahmad Masoudi, Hossein Ali Pakravan","doi":"10.1007/s11517-025-03336-8","DOIUrl":"https://doi.org/10.1007/s11517-025-03336-8","url":null,"abstract":"Bilateral internal thoracic artery (BITA) grafting utilizes both the left (LITA) and right (RITA) internal thoracic arteries simultaneously and is recommended in the literature. However, the optimal configuration for BITA grafting remains uncertain. In this study, three-dimensional numerical simulations of different BITA configurations were conducted to identify the optimal configuration and assess their performance using the fractional flow reserve (FFR), transit time flow meter (TTFM), and hemodynamic parameters. The vessel geometry of a patient who underwent a BITA grafting with a Y-graft configuration was extracted from CT angiography images, and three other configurations (pedicle, LITA as free graft, and RITA as free graft) with different degrees of stenosis were reconstructed. Results showed that, in mild to moderate stenosis (FFR > 0.7), the Y-graft configuration was less favorable for graft quality, as it had higher pulsatility index (PI) and systolic reverse flow (SRF) values, leading to increased competitive flow. However, as stenosis severity increased, these differences decreased, and for severe stenosis, the results were similar for all BITA configurations. Furthermore, the results showed that the Y-graft configuration was less effective in reducing TAWSS compared to other configurations. Oscillatory shear index (OSI) and relative residence time (RRT) did not show significant differences.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558460","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

New AI explained and validated deep learning approaches to accurately predict diabetes. 新的人工智能解释并验证了深度学习方法，以准确预测糖尿病。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-04 DOI: 10.1007/s11517-025-03338-6

Ifra Shaheen, Nadeem Javaid, Nabil Alrajeh, Yousra Asim, Syed Muhammad Abrar Akber

{"title":"New AI explained and validated deep learning approaches to accurately predict diabetes.","authors":"Ifra Shaheen, Nadeem Javaid, Nabil Alrajeh, Yousra Asim, Syed Muhammad Abrar Akber","doi":"10.1007/s11517-025-03338-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03338-6","url":null,"abstract":"Diabetes is a metabolic condition that can lead to chronic illness and organ failure if it remains untreated. Accurate detection is essential to reduce these risks at an early stage. Recent advancements in predictive models show promising results. However, these models exhibit inadequate accuracy, struggle with class imbalance, and lack interpretability of the decision-making process. To overcome these issues, we propose two novel deep models for early and accurate diabetes prediction: LeDNet (inspired by LeNet and the Dual Attention Network) and HiDenNet (influenced by the highway network and DenseNet). The models are trained using the Diabetes Health Indicators dataset, which has an inherent class imbalance problem and results in biased predictions. This imbalance is mitigated by employing the majority-weighted minority over-sampling technique. Experimental findings demonstrate that LeDNet achieves an F1-score of 85%, recall of 84%, accuracy of 85%, and precision of 86%. Similarly, HiDenNet achieves accuracy, F1-score, recall, and precision of 85%, 86%, 86%, and 86%, respectively. Both proposed models outperform the state-of-the-art deep learning (DL) models. K-fold cross-validation is applied to ensure models' stability at different data splits. Local interpretable model-agnostic explanations and Shapley additive explanations techniques are utilized to enhance interpretability and overcome the traditional black-box nature of DL models. By providing both local and global insights into feature contributions, these explainable artificial intelligence techniques provide transparency to LeDNet and HiDenNet in diabetes prediction. LeDNet and HiDenNet not only improve decision-making transparency but also enhance diabetes prediction accuracy, making them reliable tools for clinical decision-making and early diagnosis.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544183","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

Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging. 评估和增强视觉变换器在医学成像中对抗恶意攻击的鲁棒性。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-01 Epub Date: 2024-10-25 DOI: 10.1007/s11517-024-03226-5

Elif Kanca, Selen Ayas, Elif Baykal Kablan, Murat Ekinci

{"title":"Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging.","authors":"Elif Kanca, Selen Ayas, Elif Baykal Kablan, Murat Ekinci","doi":"10.1007/s11517-024-03226-5","DOIUrl":"10.1007/s11517-024-03226-5","url":null,"abstract":"Deep neural networks (DNNs) have demonstrated exceptional performance in medical image analysis. However, recent studies have uncovered significant vulnerabilities in DNN models, particularly their susceptibility to adversarial attacks that manipulate these models into making inaccurate predictions. Vision Transformers (ViTs), despite their advanced capabilities in medical imaging tasks, have not been thoroughly evaluated for their robustness against such attacks in this domain. This study addresses this research gap by conducting an extensive analysis of various adversarial attacks on ViTs specifically within medical imaging contexts. We explore adversarial training as a potential defense mechanism and assess the resilience of ViT models against state-of-the-art adversarial attacks and defense strategies using publicly available benchmark medical image datasets. Our findings reveal that ViTs are vulnerable to adversarial attacks even with minimal perturbations, although adversarial training significantly enhances their robustness, achieving over 80% classification accuracy. Additionally, we perform a comparative analysis with state-of-the-art convolutional neural network models, highlighting the unique strengths and weaknesses of ViTs in handling adversarial threats. This research advances the understanding of ViTs robustness in medical imaging and provides insights into their practical deployment in real-world scenarios.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"673-690"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511896","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

Research on imaging biomarkers for chronic subdural hematoma recurrence. 慢性硬膜下血肿复发的影像生物标志物研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-01 Epub Date: 2024-11-06 DOI: 10.1007/s11517-024-03232-7

Liyang Wu, Yvmei Zhu, Qiuyong Huang, Shuchao Chen, Haoyang Zhou, Zihao Xu, Bo Li, Hongbo Chen, Junhui Lv

{"title":"Research on imaging biomarkers for chronic subdural hematoma recurrence.","authors":"Liyang Wu, Yvmei Zhu, Qiuyong Huang, Shuchao Chen, Haoyang Zhou, Zihao Xu, Bo Li, Hongbo Chen, Junhui Lv","doi":"10.1007/s11517-024-03232-7","DOIUrl":"10.1007/s11517-024-03232-7","url":null,"abstract":"This study utilizes radiomics to explore imaging biomarkers for predicting the recurrence of chronic subdural hematoma (CSDH), aiming to improve the prediction of CSDH recurrence risk. Analyzing CT scans from 64 patients with CSDH, we extracted 107 radiomic features and employed recursive feature elimination (RFE) and the XGBoost algorithm for feature selection and model construction. The feature selection process identified six key imaging biomarkers closely associated with CSDH recurrence: flatness, surface area to volume ratio, energy, run entropy, small area emphasis, and maximum axial diameter. The selection of these imaging biomarkers was based on their significance in predicting CSDH recurrence, revealing deep connections between postoperative variables and recurrence. After feature selection, there was a significant improvement in model performance. The XGBoost model demonstrated the best classification performance, with the average accuracy improving from 46.82% (before feature selection) to 80.74% and the AUC value increasing from 0.5864 to 0.7998. These results prove that precise feature selection significantly enhances the model's predictive capability. This study not only reveals imaging biomarkers for CSDH recurrence but also provides valuable insights for future personalized treatment strategies.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"823-834"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584784","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

Numerical modeling and analysis of neck injury induced by parachute opening shock. 降落伞打开冲击对颈部伤害的数值建模和分析。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-01 Epub Date: 2024-11-07 DOI: 10.1007/s11517-024-03220-x

Feng Zhu, Liming Voo, Krithika Balakrishnan, Michael Lapera, Zhiqing Cheng

{"title":"Numerical modeling and analysis of neck injury induced by parachute opening shock.","authors":"Feng Zhu, Liming Voo, Krithika Balakrishnan, Michael Lapera, Zhiqing Cheng","doi":"10.1007/s11517-024-03220-x","DOIUrl":"10.1007/s11517-024-03220-x","url":null,"abstract":"Neck injuries from parachute opening shock (POS) are a concern in skydiving and military operations. This study employs finite element modeling to simulate POS scenarios and assess cervical spine injury risks. Validated against various conditions, including whiplash, the model replicates head/neck kinematics and soft tissue responses. POS simulations capture body/head motions during parachute deployment, indicating minimal risk of severe neck injuries (Abbreviated Injury Score/AIS ≥ 2) and low risk of soft tissue tears. Vertebral stress analysis during a rougher jump highlights high stress at C5/C6 lamina, indicating fracture risk. Comparative analysis with rear impact scenarios reveals distinct strain patterns, with rear impacts showing higher ligament strain, consistent with higher soft tissue damage risk. Though POS simulations exhibit lower strain values, they emphasize similar neck deformation patterns. The model's capability to accurately simulate head and neck movements during parachute openings provides critical validation for its use in assessing injury risks. The study's findings underline the importance of considering specific loading conditions in injury assessments and contribute to refining safety standards for skydiving and military operations. By highlighting the differences in injury mechanisms between POS and rear impacts, this research offers valuable insights into tailored injury mitigation strategies. The results not only enhance our understanding of neck injury mechanisms but also inform the development of protective gear and safety protocols, ultimately aiding in injury prevention for skydivers and military personnel.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"849-865"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590974","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

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network. 通过多视角集合学习网络进行颞下颌关节 CBCT 图像分割。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-03-01 Epub Date: 2024-10-28 DOI: 10.1007/s11517-024-03225-6

Piaolin Hu, Jupeng Li, Ruohan Ma, Kai Zhang, Yong Guo, Gang Li

{"title":"Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.","authors":"Piaolin Hu, Jupeng Li, Ruohan Ma, Kai Zhang, Yong Guo, Gang Li","doi":"10.1007/s11517-024-03225-6","DOIUrl":"10.1007/s11517-024-03225-6","url":null,"abstract":"Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179 mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550 mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"693-706"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511897","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