Medical & Biological Engineering & Computing最新文献_第5页

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration. ConKeD：基于关键点的视网膜图像配准的多视角对比描述学习。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-06 DOI: 10.1007/s11517-024-03160-6

David Rivas-Villar, Álvaro S Hervella, José Rouco, Jorge Novo

{"title":"ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.","authors":"David Rivas-Villar, Álvaro S Hervella, José Rouco, Jorge Novo","doi":"10.1007/s11517-024-03160-6","DOIUrl":"10.1007/s11517-024-03160-6","url":null,"abstract":"Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3721-3736"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538845","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

G-T correcting: an improved training of image segmentation under noisy labels. G-T 校正：噪声标签下图像分割的改进训练。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11517-024-03170-4

Yun Gao, Junhu Fu, Yi Guo, Yuanyuan Wang

{"title":"G-T correcting: an improved training of image segmentation under noisy labels.","authors":"Yun Gao, Junhu Fu, Yi Guo, Yuanyuan Wang","doi":"10.1007/s11517-024-03170-4","DOIUrl":"10.1007/s11517-024-03170-4","url":null,"abstract":"Data-driven medical image segmentation networks require expert annotations, which are hard to obtain. Non-expert annotations are often used instead, but these can be inaccurate (referred to as \"noisy labels\"), misleading the network's training and causing a decline in segmentation performance. In this study, we focus on improving the segmentation performance of neural networks when trained with noisy annotations. Specifically, we propose a two-stage framework named \"G-T correcting,\" consisting of \"G\" stage for recognizing noisy labels and \"T\" stage for correcting noisy labels. In the \"G\" stage, a positive feedback method is proposed to automatically recognize noisy samples, using a Gaussian mixed model to classify clean and noisy labels through the per-sample loss histogram. In the \"T\" stage, a confident correcting strategy and early learning strategy are adopted to allow the segmentation network to receive productive guidance from noisy labels. Experiments on simulated and real-world noisy labels show that this method can achieve over 90% accuracy in recognizing noisy labels, and improve the network's DICE coefficient to 91%. The results demonstrate that the proposed method can enhance the segmentation performance of the network when trained with noisy labels, indicating good clinical application prospects.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3781-3799"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731575","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

Radiomics of pituitary adenoma using computer vision: a review. 利用计算机视觉对垂体腺瘤进行放射组学研究：综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-16 DOI: 10.1007/s11517-024-03163-3

Tomas Zilka, Wanda Benesova

{"title":"Radiomics of pituitary adenoma using computer vision: a review.","authors":"Tomas Zilka, Wanda Benesova","doi":"10.1007/s11517-024-03163-3","DOIUrl":"10.1007/s11517-024-03163-3","url":null,"abstract":"Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of \"Radiomics\" involves the extraction of high-dimensional features, often referred to as \"Radiomic features,\" from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3581-3597"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621592","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

Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images. 用于从 CT 图像分割肺气道的细节敏感 3D-UNet

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-17 DOI: 10.1007/s11517-024-03169-x

Qin Zhang, Jiajie Li, Xiangling Nan, Xiaodong Zhang

{"title":"Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images.","authors":"Qin Zhang, Jiajie Li, Xiangling Nan, Xiaodong Zhang","doi":"10.1007/s11517-024-03169-x","DOIUrl":"10.1007/s11517-024-03169-x","url":null,"abstract":"The segmentation of airway from computed tomography (CT) images plays a vital role in pulmonary disease diagnosis, evaluation, surgical planning, and treatment. Nevertheless, it is still challenging for current methods to handle distal thin and low-contrast airways, leading to mis-segmentation issues. This paper proposes a detail-sensitive 3D-UNet (DS-3D-UNet) that incorporates two new modules into 3D-UNet to segment airways accurately from CT images. The feature recalibration module is designed to give more attention to the foreground airway features through a new attention mechanism. The detail extractor module aims to restore multi-scale detailed features by fusion of features at different levels. Extensive experiments were conducted on the ATM'22 challenge dataset composed of 300 CT scans with airway annotations to evaluate its performance. Quantitative comparisons prove that the proposed model achieves the best performance in terms of Dice similarity coefficient (92.6%) and Intersection over Union (86.3%), outperforming other state-of-the-art methods. Qualitative comparisons further exhibit the superior performance of our method in segmenting thin and confused distal bronchi. The proposed model could provide important references for the diagnosis and treatment of pulmonary diseases, holding promising prospects in the field of digital medicine. Codes are available at https://github.com/nighlevil/DS-3D-UNet/tree/master .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3749-3762"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629180","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

Continuous mobile measurement of camptocormia angle using four accelerometers. 使用四个加速度计对凸轮角进行连续移动测量。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-06-27 DOI: 10.1007/s11517-024-03149-1

K Naderi Beni, K Knutzen, J P Kuhtz-Buschbeck, N G Margraf, R Rieger

{"title":"Continuous mobile measurement of camptocormia angle using four accelerometers.","authors":"K Naderi Beni, K Knutzen, J P Kuhtz-Buschbeck, N G Margraf, R Rieger","doi":"10.1007/s11517-024-03149-1","DOIUrl":"10.1007/s11517-024-03149-1","url":null,"abstract":"Camptocormia, a severe flexion deformity of the spine, presents challenges in monitoring its progression outside laboratory settings. This study introduces a customized method utilizing four inertial measurement unit (IMU) sensors for continuous recording of the camptocormia angle (CA), incorporating both the consensual malleolus and perpendicular assessment methods. The setup is wearable and mobile and allows measurements outside the laboratory environment. The practicality for measuring CA across various activities is evaluated for both the malleolus and perpendicular method in a mimicked Parkinson disease posture. Multiple activities are performed by a healthy volunteer. Measurements are compared against a camera-based reference system. Results show an overall root mean squared error (RMSE) of 4.13° for the malleolus method and 2.71° for the perpendicular method. Furthermore, patient-specific calibration during the standing still with forward lean activity significantly reduced the RMSE to 2.45° and 1.68° respectively. This study presents a novel approach to continuous CA monitoring outside the laboratory setting. The proposed system is suitable as a tool for monitoring the progression of camptocormia and for the first time implements the malleolus method with IMU. It holds promise for effectively monitoring camptocormia at home.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3637-3652"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11569011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460384","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

Measurement of left ventricular volume with admittance incorporated onto percutaneous ventricular assist device. 利用经皮心室辅助装置上的导入装置测量左心室容积。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-15 DOI: 10.1007/s11517-024-03168-y

Luis A Diaz Sanmartin, Aleksandra B Gruslova, Drew R Nolen, Marc D Feldman, Jonathan W Valvano

{"title":"Measurement of left ventricular volume with admittance incorporated onto percutaneous ventricular assist device.","authors":"Luis A Diaz Sanmartin, Aleksandra B Gruslova, Drew R Nolen, Marc D Feldman, Jonathan W Valvano","doi":"10.1007/s11517-024-03168-y","DOIUrl":"10.1007/s11517-024-03168-y","url":null,"abstract":"Percutaneous ventricular assist devices (pVADs) incorporated with admittance electrodes have been validated in animal studies for accurate instantaneous volumetric measurements. Since miniaturization of the pVAD profile is a priority to reduce vascular complications in patients, our study aimed to validate admittance measurements using three electrodes instead of the standard four. Complex admittance was measured between an electrode pair and a pVAD metallic blood-intake tip, both with finite element analysis and on the benchtop. The catheter and electrode arrays were first simulated inside prolate ellipsoid models of the left ventricle (LV) demonstrating current flow throughout all parts of the LV as well as minimal influence of off-center catheter placement in the recorded signal. Admittance measurements were validated in 3D-printed models of healthy and dilated hearts (100-400 mL end-diastolic volumes). Minimal interference between a pVAD motor and the current signal of our admittance system was demonstrated. A modified Wei's equation focused on three electrodes was developed to be compatible with reduced profile pVADs occurring clinically, incorporated with admittance electrodes and wires. The modified equation was compared against Wei's original equation showing improved accuracy of calculated volumes. Reducing electrode footprint can simplify the incorporation of Admittance technology on any pVAD, allowing for instantaneous recognition of native heart recovery and assistance with pVAD weaning.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3737-3747"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617501","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

Breast tumor segmentation via deep correlation analysis of multi-sequence MRI. 通过多序列磁共振成像的深度关联分析进行乳腺肿瘤分割。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11517-024-03166-0

Hongyu Wang, Tonghui Wang, Yanfang Hao, Songtao Ding, Jun Feng

{"title":"Breast tumor segmentation via deep correlation analysis of multi-sequence MRI.","authors":"Hongyu Wang, Tonghui Wang, Yanfang Hao, Songtao Ding, Jun Feng","doi":"10.1007/s11517-024-03166-0","DOIUrl":"10.1007/s11517-024-03166-0","url":null,"abstract":"Precise segmentation of breast tumors from MRI is crucial for breast cancer diagnosis, as it allows for detailed calculation of tumor characteristics such as shape, size, and edges. Current segmentation methodologies face significant challenges in accurately modeling the complex interrelationships inherent in multi-sequence MRI data. This paper presents a hybrid deep network framework with three interconnected modules, aimed at efficiently integrating and exploiting the spatial-temporal features among multiple MRI sequences for breast tumor segmentation. The first module involves an advanced multi-sequence encoder with a densely connected architecture, separating the encoding pathway into multiple streams for individual MRI sequences. To harness the intricate correlations between different sequence features, we propose a sequence-awareness and temporal-awareness method that adeptly fuses spatial-temporal features of MRI in the second multi-scale feature embedding module. Finally, the decoder module engages in the upsampling of feature maps, meticulously refining the resolution to achieve highly precise segmentation of breast tumors. In contrast to other popular methods, the proposed method learns the interrelationships inherent in multi-sequence MRI. We justify the proposed method through extensive experiments. It achieves notable improvements in segmentation performance, with Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Positive Predictive Value (PPV) scores of 80.57%, 74.08%, and 84.74% respectively.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3801-3814"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731574","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 depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction. 深入分析利用人工智能方法进行癌症预测的非侵入性技术的最新创新。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-16 DOI: 10.1007/s11517-024-03158-0

Hari Mohan Rai, Joon Yoo, Abdul Razaque

{"title":"A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.","authors":"Hari Mohan Rai, Joon Yoo, Abdul Razaque","doi":"10.1007/s11517-024-03158-0","DOIUrl":"10.1007/s11517-024-03158-0","url":null,"abstract":"The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3555-3580"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621591","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

Hemodynamic effects of pulsatile frequency of right ventricular assist device (RVAD) on pulmonary perfusion: a simulation study. 右心室辅助装置 (RVAD) 脉动频率对肺灌注的血流动力学影响：一项模拟研究。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-25 DOI: 10.1007/s11517-024-03174-0

Fan Meng, Yuanfei Zhu, Ming Yang

{"title":"Hemodynamic effects of pulsatile frequency of right ventricular assist device (RVAD) on pulmonary perfusion: a simulation study.","authors":"Fan Meng, Yuanfei Zhu, Ming Yang","doi":"10.1007/s11517-024-03174-0","DOIUrl":"10.1007/s11517-024-03174-0","url":null,"abstract":"Right ventricular assist devices (RVADs) have been extensively used to provide hemodynamic support for patients with end-stage right heart (RV) failure. However, conventional in-parallel RVADs can lead to an elevation of pulmonary artery (PA) pressure, consequently increasing the right ventricular (RV) afterload, which is unfavorable for the relaxation of cardiac muscles and reduction of valve complications. The aim of this study is to investigate the hemodynamic effects of the pulsatile frequency of the RVAD on pulmonary artery. Firstly, a mathematical model incorporating heart, systemic circulation, pulmonary circulation, and RVAD is developed to simulate the cardiovascular system. Subsequently, the frequency characteristics of the pulmonary circulation system are analyzed, and the calculated results demonstrate that the pulsatile frequency of the RVAD has a substantive impact on the pulmonary artery pressure. Finally, to verify the analysis results, the hemodynamic effects of the pulsatile frequency of the RVAD on pulmonary artery are compared under diffident support modes. It is found that the pulmonary artery pressure decreases by approximately 6% when the pulsatile frequency changes from 1 to 3 Hz. The increased pulsatile frequency of RA-PA support mode may facilitate the opening of the pulmonary valve, while the RV-PA support mode can more effectively reduce the load of RV. This work provides a useful method to decrease the pulmonary artery pressure during the RVAD supports and may be beneficial for improving myocardial function in patients with end-stage right heart failure, especially those with pulmonary hypertension.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3875-3885"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762149","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

Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI. 从核磁共振成像中检测和分级腰椎间盘突出症的增强型深度倾斜模型

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-05 DOI: 10.1007/s11517-024-03161-5

Xianyin Duan, Hanlin Xiong, Rong Liu, Xianbao Duan, Haotian Yu

{"title":"Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.","authors":"Xianyin Duan, Hanlin Xiong, Rong Liu, Xianbao Duan, Haotian Yu","doi":"10.1007/s11517-024-03161-5","DOIUrl":"10.1007/s11517-024-03161-5","url":null,"abstract":"Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3709-3719"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535776","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