Medical & Biological Engineering & Computing最新文献

{"title":"VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.","authors":"Mengxin Li, Fan Lv, Jiaming Chen, Kunyan Zheng, Jingwen Zhao","doi":"10.1007/s11517-024-03219-4","DOIUrl":"10.1007/s11517-024-03219-4","url":null,"abstract":"<p><p>Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongated blood vessels in the brain, and it is easy to lose the feature information of the elongated blood vessels during the network training process. In this paper, a vascular convolutional U-network (VCU-Net) is proposed to address these problems. This network utilizes a new convolution (vascular convolution) instead of the traditional convolution kernel, to extract features of elongated blood vessels in the brain with different morphologies and orientations by adaptive convolution. In the network encoding stage, a new feature splicing method is used to combine the feature tensor obtained through vascular convolution with the original tensor to provide richer feature information. Experiments show that the DSC and IOU of the proposed method are 53.57% and 69.74%, which are improved by 2.11% and 2.01% over the best performance of the GVC-Net among several typical models. In image visualization, the proposed network has better segmentation performance for complex cerebrovascular structures, especially in dealing with elongated blood vessels in the brain, which shows better integrity and continuity.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"661-672"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511898","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":"Unsupervised cervical cell instance segmentation method integrating cellular characteristics.","authors":"Yining Xie, Jingling Gao, Xueyan Bi, Jing Zhao","doi":"10.1007/s11517-024-03222-9","DOIUrl":"10.1007/s11517-024-03222-9","url":null,"abstract":"<p><p>Cell instance segmentation is a key technology for cervical cancer auxiliary diagnosis systems. However, pixel-level annotation is time-consuming and labor-intensive, making it difficult to obtain a large amount of annotated data. This results in the model not being fully trained. In response to these problems, this paper proposes an unsupervised cervical cell instance segmentation method that integrates cell characteristics. Cervical cells have a clear corresponding structure between the nucleus and cytoplasm. This method fully takes this feature into account by building a dual-flow framework to locate the nucleus and cytoplasm and generate high-quality pseudo-labels. In the nucleus segmentation stage, the position and range of the nucleus are determined using the standard cell-restricted nucleus segmentation method. In the cytoplasm segmentation stage, a multi-angle collaborative segmentation method is used to achieve the positioning of the cytoplasm. First, taking advantage of the self-similarity characteristics of pixel blocks in cells, a cytoplasmic segmentation method based on self-similarity map iteration is proposed. The pixel blocks are mapped from the perspective of local details, and the iterative segmentation is repeated. Secondly, using low-level features such as cell color and shape, a self-supervised heatmap-aware cytoplasm segmentation method is proposed to obtain the activation map of the cytoplasm from the perspective of global attention. The two methods are fused to determine cytoplasmic regions, and combined with nuclear locations, high-quality pseudo-labels are generated. These pseudo-labels are used to train the model cyclically, and the loss strategy is used to encourage the model to discover new object masks, thereby obtaining a segmentation model with better performance. Experimental results show that this method achieves good results in cytoplasm segmentation. On the three datasets of ISBI, MS_CellSeg, and Cx22, 54.32%, 44.64%, and 66.52% AJI were obtained, respectively, which is better than other typical unsupervised methods selected in this article.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"773-791"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570345","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":"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":"867-883"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","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":"Structural design and biomechanical analysis of a combined titanium and polyetheretherketone cage based on PE-PLIF fusion.","authors":"Lei Ma, Yutang Xie, Kai Zhang, Jing Chen, Yanqin Wang, Liming He, Haoyu Feng, Weiyi Chen, Meng Zhang, Yanru Xue, Xiaogang Wu, Qiang Li","doi":"10.1007/s11517-024-03214-9","DOIUrl":"10.1007/s11517-024-03214-9","url":null,"abstract":"<p><p>In lumbar spinal fusion, the titanium cage tends to cause stress shielding due to their high elastic modulus, which can lead to degenerative lesions in adjacent spinal segments. Furthermore, polyetheretherketone (PEEK) cages have certain material characteristics that do not promote bone ingrowth and fusion stability. In this study, a new cage was designed, and its biomechanical performance in percutaneous endoscopic posterior lumbar interbody fusion (PE-PLIF) was analyzed using the finite element (FE) method. A complete model of the L4-L5 lumbar spine was established, and static and harmonic vibration FE analysis models were developed based on it. The biomechanical properties of titanium, PEEK, and combined cage in PE-PLIF fusion were compared. The strain capacity of the combined fusion increased by 9.5% when compared to the titanium fusion. The surgical model under the combined fusion reduces the L5 endplate stress by 12% in the forward flexion condition and the fusion stress by 17% in the vibration condition compared to the model supported by the titanium fusion, and the differences in screw stress and mobility among the three models are not significant in multiple conditions. Consequently, the combined cage demonstrates a certain reduction in the stress-shielding effect when compared to the titanium cage; it has better fusion effect and provides theoretical support and guidance for the design of the clinical fusion cage.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"707-720"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523496","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":"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":"885-899"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","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":"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":"901-914"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","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":"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":"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":"915-932"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","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":"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}