Medical & Biological Engineering & Computing最新文献

{"title":"Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.","authors":"Hailiang Ye, Siqi Liu, Ming Li, Houying Zhu, Feilong Cao","doi":"10.1007/s11517-024-03242-5","DOIUrl":"https://doi.org/10.1007/s11517-024-03242-5","url":null,"abstract":"<p><p>The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effectively exploit the information contained in the complex spatial structure of the cortical surface and generally encountered an uneven node distribution issue. Meanwhile, labeling boundary nodes was also identified as a widespread problem in this task. Accordingly, this paper develops a scale-unified spatial learning network with a boundary contrastive loss (SSLNet) for cortical surface parcellation. Its core is the scale-unified spatial learning module. It devises neighbor feature extraction and aggregation strategies by fully integrating spatial coordinates and semantic structure to learn effective spatial features of local neighborhoods. More importantly, spatial scale unification is incorporated into this module to mitigate the negative effect on spatial learning caused by node distribution differences among local areas. Additionally, a universal boundary contrastive loss is constructed, enhancing the feature discriminability of boundary nodes by constraining them to be close to the same class nodes and apart from different class nodes in the feature space. It considerably improves boundary performance without increasing parameters or changing the network structure. Experiments regarding public Mindboggle demonstrate that the dice score and accuracy of SSLNet achieve <math><mrow><mn>89.8</mn> <mo>%</mo></mrow> </math> and <math><mrow><mn>90.89</mn> <mo>%</mo></mrow> </math> , respectively, surpassing existing methods.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645093","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}

{"title":"Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach.","authors":"Zeynel A Samak","doi":"10.1007/s11517-024-03243-4","DOIUrl":"https://doi.org/10.1007/s11517-024-03243-4","url":null,"abstract":"<p><p>Stroke, a major cause of death and disability worldwide, can be haemorrhagic or ischaemic depending on the type of bleeding in the brain. Rapid and accurate identification of stroke type and lesion segmentation is critical for timely and effective treatment. However, existing research primarily focuses on segmenting a single stroke type, potentially limiting their clinical applicability. This study addresses this gap by exploring multi-type stroke lesion segmentation using deep learning methods. Specifically, we investigate two distinct approaches: a single-stage approach that directly segments all tissue types in one model and a hierarchical approach that first classifies stroke types and then utilises specialised segmentation models for each subtype. Recognising the importance of accurate stroke classification for the hierarchical approach, we evaluate ResNet, ResNeXt and ViT networks, incorporating focal loss and oversampling techniques to mitigate the impact of class imbalance. We further explore the performance of U-Net, U-Net++ and DeepLabV3 models for segmentation within each approach. We use a comprehensive dataset of 6650 images provided by the Ministry of Health of the Republic of Türkiye. This dataset includes 1130 ischaemic strokes, 1093 haemorrhagic strokes and 4427 non-stroke cases. In our comparative experiments, we achieve an AUC score of 0.996 when classifying stroke and non-stroke slices. For lesion segmentation task, while the performance of different architectures is comparable, the hierarchical training approach outperforms the single-stage approach in terms of intersection over union (IoU). The performance of the U-Net model increased significantly from an IoU of 0.788 to 0.875 when the hierarchical approach is used. This comparative analysis aims to identify the most effective approach and deep learning model for multi-type stroke lesion segmentation in brain CT scans, potentially leading to improved clinical decision-making, treatment efficiency and outcomes.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645092","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}

{"title":"Effects of 1800 MHz and 2100 MHz mobile phone radiation on the blood-brain barrier of New Zealand rabbits.","authors":"Abdullah Oğuz Kizilçay, Bilal Tütüncü, Mehmet Koçarslan, Mahmut Ahmet Gözel","doi":"10.1007/s11517-024-03238-1","DOIUrl":"https://doi.org/10.1007/s11517-024-03238-1","url":null,"abstract":"<p><p>In this study, the impact of mobile phone radiation on blood-brain barrier (BBB) permeability was investigated. A total of 21 New Zealand rabbits were used for the experiments, divided into three groups, each consisting of 7 rabbits. One group served as the control, while the other two were exposed to electromagnetic radiation at frequencies of 1800 MHz with a distance of 14.5 cm and 2100 MHz with a distance of 17 cm, maintaining a constant power intensity of 15 dBm, for a duration equivalent to the current average daily conversation time of 38 min. The exposure was conducted under non-thermal conditions, with RF radiation levels approximately ten times lower than normal values. Evans blue (EB) dye was used as a marker to assess BBB permeability. EB binds to plasma proteins, and its presence in brain tissue indicates a disruption in BBB integrity, allowing for a quantitative evaluation of radiation-induced permeability changes. Left and right brain tissue samples were analyzed using trichloroacetic acid (TCA) and phosphate-buffered solution (PBS) solutions to measure EB amounts at 620 nm via spectrophotometry. After the experiments, BBB tissue samples were collected from the right and left brains of all rabbits in the three groups and subjected to a series of medical procedures. Samples from Group 1 were compared with those from Group 2 and Group 3 using statistical methods to determine if there were any significant differences. As a result, it was found that there was no statistically significant difference in the BBB of rabbits exposed to 1800 MHz radiation, whereas there was a statistically significant difference at a 95% confidence level in the BBB of rabbits exposed to 2100 MHz radiation. A decrease in EB values was observed upon the arithmetic examination of the BBB.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640090","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}

{"title":"An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.","authors":"Archana B, K Kalirajan","doi":"10.1007/s11517-024-03237-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03237-2","url":null,"abstract":"<p><p>Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into four stages: Non-demented (ND), moderate demented (MoD), mild demented (MiD), and very mild demented (VMD). To prepare the raw dataset for analysis, the collected magnetic resonance imaging (MRI) images are subjected to several pre-processing techniques in order to improve the performance accuracy of the proposed model. Medical images generally have poor contrast and get affected by noise, which ends up with inaccurate diagnosis. For the different phases of AD to be detected, a clear image is necessary. To address this issue, the influence of the artefacts must be reduced, enhance the contrast, and reduce the loss of information. A novel framework for image enhancement is suggested to increase the accuracy in the detection and identification of AD. In this study, the raw MRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) database is subjected to skull stripping, contrast enhancement, and image filtering followed by data augmentation to balance the dataset with four types of Alzheimer's classes. The pre-processed data are subjected to five different pre-trained models such as AlexNet, ResNet, VGG 16, EfficientNet, and Inceptionv3 achieving a testing accuracy rate of 91.2%, 88.21%, 92.34%, 93.45%, and 85.12%, respectively. These pre-trained models are compared with the proposed CNN (convolutional neural network) model designed with Adam optimizer and Flatten Swish activation function which reaches the highest accuracy of 96.5% with a learning rate of 0.000001. The five pre-trained CNN models along with the proposed swish-based AD-CNN were tested using various performance metrics to evaluate the model efficiency in classifying and identifying the AD classes. From the result analysis, it is evident that the proposed AD-CNN model outperforms all the other models.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640088","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}

{"title":"A non-invasive heart rate prediction method using a convolutional approach.","authors":"Ercument Karapinar, Ender Sevinc","doi":"10.1007/s11517-024-03217-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03217-6","url":null,"abstract":"<p><p>The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631363","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}

{"title":"Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.","authors":"Jinming Zhang, He Gong, Pengling Ren, Shuyu Liu, Zhengbin Jia, Peipei Shi","doi":"10.1007/s11517-024-03239-0","DOIUrl":"https://doi.org/10.1007/s11517-024-03239-0","url":null,"abstract":"<p><p>The purpose of this study is to quantify the three-dimensional (3D) structural morphology, bone mineral density (BMD) distribution, and mechanical properties of different China-Japan Friendship Hospital (CJFH) classification types and assist clinicians in classifying necrotic femurs accurately. In this study, 41 cases were classified as types L2 and L3 based on CT images. Then, 3D Statistical Shape and Appearance Models (SSM and SAM) were established, and 80 principal component (PC) modes were extracted from the SSM and SAM as the candidate features. The bone strength of each case was also calculated as the candidate feature using finite element analysis (FEA). Support vector machine (SVM) and Extreme Gradient Boosting (XGBoost) were used to establish 10 machine learning models. Feature selection methods were used to screen the candidate features. The performance of each model was evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. This resulted in a SVM model for CJFH classification with the performance: accuracy of 87.5%, sensitivity of 85.0%, specificity of 76.0%, and AUC of 94.2%. This study provided effective machine learning models for assisting in diagnosing CJFH types, increasing the objectivity of the diagnosis. They may have great potential for application in clinical assessments of CJFH classification.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631366","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}

{"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":"https://doi.org/10.1007/s11517-024-03220-x","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-07","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}

{"title":"Correction to: 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/s11517-024-03240-7","DOIUrl":"https://doi.org/10.1007/s11517-024-03240-7","url":null,"abstract":"","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607258","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}

{"title":"Mark3D - A semi-automated open-source toolbox for 3D head- surface reconstruction and electrode position registration using a smartphone camera video.","authors":"Suranjita Ganguly, Malaaika Mihir Chhaya, Ankita Jain, Aditya Koppula, Mohan Raghavan, Kousik Sarathy Sridharan","doi":"10.1007/s11517-024-03228-3","DOIUrl":"https://doi.org/10.1007/s11517-024-03228-3","url":null,"abstract":"<p><p>Source localization in EEG necessitates co-registering the EEG sensor locations with the subject's MRI, where EEG sensor locations are typically captured using electromagnetic tracking or 3D scanning of the subject's head with EEG cap, using commercially available 3D scanners. Both methods have drawbacks, where, electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative but requires multiple photos to sample the head, with good spatial sampling to adequately reconstruct the head surface. Post-reconstruction, the existing tools for electrode position labelling on the 3D head-surface have limited visual feedback and do not easily accommodate customized montages, which are typical in multi-modal measurements. We introduce Mark3D, an open-source, integrated tool for 3D head-surface reconstruction from phone camera video. It eliminates the need for keeping track of spatial sampling during image capture for video-based photogrammetry reconstruction. It also includes blur detection algorithms, a user-friendly interface for electrode and tracking, and integrates with popular toolboxes such as FieldTrip and MNE Python. The accuracy of the proposed method was benchmarked with the head-surface derived from a commercially available handheld 3D scanner Einscan-Pro + (Shining 3D Inc.,) which we treat as the \"ground truth\". We used reconstructed head-surfaces of ground truth (G1) and phone camera video (M₁₀₈₀) to mark the EEG electrode locations in 3D space using a dedicated UI provided in the tool. The electrode locations were then used to form pseudo-specific MRI templates for individual subjects to reconstruct source information. Somatosensory source activations in response to vibrotactile stimuli were estimated and compared between G1 and M₁₀₈₀. The mean positional errors of the EEG electrodes between G1 and M₁₀₈₀ in 3D space were found to be 0.09 ± 0.01 mm across different cortical areas, with temporal and occipital areas registering a relatively higher error than other regions such as frontal, central or parietal areas. The error in source reconstruction was found to be 0.033 ± 0.016 mm and 0.037 ± 0.017 mm in the left and right cortical hemispheres respectively.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607259","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}

{"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":"https://doi.org/10.1007/s11517-024-03232-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-06","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}