Medical & Biological Engineering & Computing最新文献

Strain energy in human tibia during different exercises with adjustable leg weights: a subject-specific computational model analysis.

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.

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.

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.

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.

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

ETDformer: an effective transformer block for segmentation of intracranial hemorrhage.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-28 DOI: 10.1007/s11517-025-03333-x

Wanyuan Gong, Yanmin Luo, Fuxing Yang, Huabiao Zhou, Zhongwei Lin, Chi Cai, Youcao Lin, Junyan Chen

{"title":"ETDformer: an effective transformer block for segmentation of intracranial hemorrhage.","authors":"Wanyuan Gong, Yanmin Luo, Fuxing Yang, Huabiao Zhou, Zhongwei Lin, Chi Cai, Youcao Lin, Junyan Chen","doi":"10.1007/s11517-025-03333-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03333-x","url":null,"abstract":"Intracerebral hemorrhage (ICH) medical image segmentation plays a crucial role in clinical diagnostics and treatment planning. The U-Net architecture, known for its encoder-decoder design and skip connections, is widely used but often struggles with accurately delineating complex struct ures like ICH regions. Recently, transformer models have been incorporated into medical image segmentation, improving performance by capturing long-range dependencies. However, existing methods still face challenges in incorrectly segmenting non-target areas and preserving detailed information in the target region. To address these issues, we propose a novel segmentation model that combines U-Net's local feature extraction with the transformer's global perceptiveness. Our method introduces an External Storage Module (ES Module) to capture and store feature similarities between adjacent slices, and a Top-Down Attention (TDAttention) mechanism to focus on relevant lesion regions while enhancing target boundary segmentation. Additionally, we introduce a boundary DoU loss to improve lesion boundary delineation. Evaluations on the intracranial hemorrhage dataset (IHSAH) from the Second Affiliated Hospital of Fujian Medical University, as well as the publicly available Brain Hemorrhage Segmentation Dataset (BHSD), demonstrate that our approach achieves DSC scores of 91.29% and 85.10% on the IHSAH and BHSD datasets, respectively, outperforming the second-best Cascaded MERIT by 2.19% and 2.05%, respectively. Moreover, our method provides enhanced visualization of lesion details, significantly aiding diagnostic accuracy.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525059","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

Estimation of pulse wave analysis indices from invasive arterial blood pressure only for a clinical assessment of wave reflection in a 5-day septic animal experiment.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-27 DOI: 10.1007/s11517-025-03328-8

Diletta Guberti, Manuela Ferrario, Marta Carrara

{"title":"Estimation of pulse wave analysis indices from invasive arterial blood pressure only for a clinical assessment of wave reflection in a 5-day septic animal experiment.","authors":"Diletta Guberti, Manuela Ferrario, Marta Carrara","doi":"10.1007/s11517-025-03328-8","DOIUrl":"https://doi.org/10.1007/s11517-025-03328-8","url":null,"abstract":"Wave separation analysis (WSA) is the gold standard to analyze the arterial blood pressure (ABP) waveform, decomposing it into a forward and a reflected wave. It requires ABP and arterial blood flow (ABF) measurement, and ABF is often unavailable in clinical settings. Therefore, methods to estimate ABF from ABP have been proposed, but they are not investigated in critical conditions. In this work, an autoregressive with exogenous input model was proposed as an original method to estimate ABF from the measured ABP. Its performance in assessing WSA indices to characterize the arterial tree was evaluated in critical conditions, i.e., during sepsis. The triangular and the personalized flow approximation and the multi-Gaussian ABP decomposition were compared to the proposed model. The results highlighted how the black-box modeling approach is superior to other flow estimation models when computing WSA indices in septic condition. This approach holds promise for overcoming challenges in clinical settings where ABF data are unavailable.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517154","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 placement of simulated dental implants within CBCT images in optimum positions: a deep learning model.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-26 DOI: 10.1007/s11517-025-03327-9

Shahd Alotaibi, Mona Alsomali, Shatha Alghamdi, Sara Alfadda, Isra Alturaiki, Asma'a Al-Ekrish, Najwa Altwaijry

{"title":"Automatic placement of simulated dental implants within CBCT images in optimum positions: a deep learning model.","authors":"Shahd Alotaibi, Mona Alsomali, Shatha Alghamdi, Sara Alfadda, Isra Alturaiki, Asma'a Al-Ekrish, Najwa Altwaijry","doi":"10.1007/s11517-025-03327-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03327-9","url":null,"abstract":"Implant dentistry is the standard of care for the replacement of missing teeth. It is a complex process where cone-beam computed tomography (CBCT) images are analyzed by the dentist to determine the implants' length, diameter, and position, and angulation diameter, position, and angulation taking into consideration the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. This traditional procedure is time-consuming and relies heavily on the dentist's knowledge and expertise, which makes it subject to human errors. This study presents a two-stage framework for the placement of dental implants. The first stage utilizes YOLOv11 for the detection of fiducial markers and adjacent bone within 2D slices of 3D CBCT images. In the second stage, classification and regression are applied to extract the apical and occlusal coordinates of the implants and to predict the implants' intra-osseous length and intra-osseous diameter. YOLOv11 achieved a 59% F-score in the marker detection phase. The mean absolute error for the implant position prediction ranged from 11.931 to 15.954. The classification of the intra-osseous diameter showed 76% accuracy, and the intra-osseous length showed an accuracy of 59%. Our results were reviewed by an expert prosthodontist and deemed promising.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504824","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

M4S-Net: a motion-enhanced shape-aware semi-supervised network for echocardiography sequence segmentation.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub 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":"https://doi.org/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":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-24","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

LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-24 DOI: 10.1007/s11517-025-03326-w

Mingjing Yang, Kangwen Yang, Mengjun Wu, Liqin Huang, Wangbin Ding, Lin Pan, Lei Yin

{"title":"LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation.","authors":"Mingjing Yang, Kangwen Yang, Mengjun Wu, Liqin Huang, Wangbin Ding, Lin Pan, Lei Yin","doi":"10.1007/s11517-025-03326-w","DOIUrl":"https://doi.org/10.1007/s11517-025-03326-w","url":null,"abstract":"Myocardium scar segmentation is essential for clinical diagnosis and prognosis for cardiac vascular diseases. Late gadolinium enhancement (LGE) imaging technology has been widely utilized to visualize left atrial and ventricular scars. However, automatic scar segmentation remains challenging due to the imbalance between scar and background and the variation in scar sizes. To address these challenges, we introduce an innovative network, i.e., LGENet, for scar segmentation. LGENet disentangles anatomy and pathology features from LGE images. Note that inherent spatial relationships exist between the myocardium and scarring regions. We proposed a boundary attention module to allow the scar segmentation conditioned on anatomical boundary features, which could mitigate the imbalance problem. Meanwhile, LGENet can predict scar regions across multiple scales with a multi-depth decision module, addressing the scar size variation issue. In our experiments, we thoroughly evaluated the performance of LGENet using LAScarQS 2022 and EMIDEC datasets. The results demonstrate that LGENet achieved promising performance for cardiac scar segmentation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484491","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