Medical & Biological Engineering & Computing最新文献_第2页

Comparative biomechanical analysis of a conventional/novel hip prosthetic socket. 传统/新型髋关节假体髋臼的生物力学比较分析。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-03 DOI: 10.1007/s11517-024-03206-9

Yu Qian, Yunzhang Cheng, Shiyao Chen, Mingwei Zhang, Yingyu Fang, Tianyi Zhang

引用次数: 0

HF-CMN: a medical report generation model for heart failure. HF-CMN：心力衰竭医疗报告生成模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-03 DOI: 10.1007/s11517-024-03197-7

Liangquan Yan, Jumin Zhao, Danyang Shi, Dengao Li, Yi Liu

{"title":"HF-CMN: a medical report generation model for heart failure.","authors":"Liangquan Yan, Jumin Zhao, Danyang Shi, Dengao Li, Yi Liu","doi":"10.1007/s11517-024-03197-7","DOIUrl":"10.1007/s11517-024-03197-7","url":null,"abstract":"Heart failure represents the ultimate stage in the progression of diverse cardiac ailments. Throughout the management of heart failure, physicians require observation of medical imagery to formulate therapeutic regimens for patients. Automated report generation technology serves as a tool aiding physicians in patient management. However, previous studies failed to generate targeted reports for specific diseases. To produce high-quality medical reports with greater relevance across diverse conditions, we introduce an automatic report generation model HF-CMN, tailored to heart failure. Firstly, the generated report includes comprehensive information pertaining to heart failure gleaned from chest radiographs. Additionally, we construct a storage query matrix grouping based on a multi-label type, enhancing the accuracy of our model in aligning images with text. Experimental results demonstrate that our method can generate reports strongly correlated with heart failure and outperforms most other advanced methods on benchmark datasets MIMIC-CXR and IU X-Ray. Further analysis confirms that our method achieves superior alignment between images and texts, resulting in higher-quality reports.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"399-415"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367227","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 multi-scale feature extraction and fusion-based model for retinal vessel segmentation in fundus images. 基于多尺度特征提取和融合的眼底图像视网膜血管分割模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-21 DOI: 10.1007/s11517-024-03223-8

Jinzhi Zhou, Guangcen Ma, Haoyang He, Saifeng Li, Guopeng Zhang

引用次数: 0

Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography. 超声心动图中用于精确左心室分割的等高线约束分支 U-Net

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-17 DOI: 10.1007/s11517-024-03201-0

Mingjun Qu, Jinzhu Yang, Honghe Li, Yiqiu Qi, Qi Yu

{"title":"Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography.","authors":"Mingjun Qu, Jinzhu Yang, Honghe Li, Yiqiu Qi, Qi Yu","doi":"10.1007/s11517-024-03201-0","DOIUrl":"10.1007/s11517-024-03201-0","url":null,"abstract":"Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnostic parameters are derived from the segmentation results, such as ejection function. However, echocardiography typically has a lower resolution and contains a significant amount of noise and motion artifacts, making it a challenge to accurate segmentation, especially in the region of the cardiac chamber boundary, which significantly restricts the accurate calculation of subsequent clinical parameters. In this paper, our goal is to achieve accurate LV segmentation through a simplified approach by introducing a branch sub-network into the decoder of the traditional U-Net. Specifically, we employed the LV contour features to supervise the branch decoding process and used a cross attention module to facilitate the interaction relationship between the branch and the original decoding process, thereby improving the segmentation performance in the region LV boundaries. In the experiments, the proposed branch U-Net (BU-Net) demonstrated superior performance on CAMUS and EchoNet-dynamic public echocardiography segmentation datasets in comparison to state-of-the-art segmentation models, without the need for complex residual connections or transformer-based architectures. Our codes are publicly available at Anonymous Github https://anonymous.4open.science/r/Anoymous_two-BFF2/ .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"561-573"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479176","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

An improved algorithm for salient object detection of microscope based on U²-Net. 基于 U2-Net 的显微镜突出物检测改进算法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-09-26 DOI: 10.1007/s11517-024-03205-w

Yunchai Li, Run Fang, Nangang Zhang, Chengsheng Liao, Xiaochang Chen, Xiaoyu Wang, Yunfei Luo, Leheng Li, Min Mao, Yunlong Zhang

{"title":"An improved algorithm for salient object detection of microscope based on U2-Net.","authors":"Yunchai Li, Run Fang, Nangang Zhang, Chengsheng Liao, Xiaochang Chen, Xiaoyu Wang, Yunfei Luo, Leheng Li, Min Mao, Yunlong Zhang","doi":"10.1007/s11517-024-03205-w","DOIUrl":"10.1007/s11517-024-03205-w","url":null,"abstract":"With the rapid advancement of modern medical technology, microscopy imaging systems have become one of the key technologies in medical image analysis. However, manual use of microscopes presents issues such as operator dependency, inefficiency, and time consumption. To enhance the efficiency and accuracy of medical image capture and reduce the burden of subsequent quantitative analysis, this paper proposes an improved microscope salient object detection algorithm based on U2-Net, incorporating deep learning technology. The improved algorithm first enhances the network's key information extraction capability by incorporating the Convolutional Block Attention Module (CBAM) into U2-Net. It then optimizes network complexity by constructing a Simple Pyramid Pooling Module (SPPM) and uses Ghost convolution to achieve model lightweighting. Additionally, data augmentation is applied to the slides to improve the algorithm's robustness and generalization. The experimental results show that the size of the improved algorithm model is 72.5 MB, which represents a 56.85% reduction compared to the original U2-Net model size of 168.0 MB. Additionally, the model's prediction accuracy has increased from 92.24 to 97.13%, providing an efficient means for subsequent image processing and analysis tasks in microscopy imaging systems.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"383-397"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331240","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

Classification of diabetic retinopathy algorithm based on a novel dual-path multi-module model. 基于新型双路径多模块模型的糖尿病视网膜病变分类算法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-09-25 DOI: 10.1007/s11517-024-03194-w

Lirong Zhang, Jialin Gang, Jiangbo Liu, Hui Zhou, Yao Xiao, Jiaolin Wang, Yuyang Guo

{"title":"Classification of diabetic retinopathy algorithm based on a novel dual-path multi-module model.","authors":"Lirong Zhang, Jialin Gang, Jiangbo Liu, Hui Zhou, Yao Xiao, Jiaolin Wang, Yuyang Guo","doi":"10.1007/s11517-024-03194-w","DOIUrl":"10.1007/s11517-024-03194-w","url":null,"abstract":"Diabetic retinopathy is a chronic disease of the eye that is precipitated via diabetes. As the disease progresses, the blood vessels in the retina are issue to modifications such as dilation, leakage, and new blood vessel formation. Early detection and treatment of the lesions are vital for the prevention and reduction of imaginative and prescient loss. A new dual-path multi-module network algorithm for diabetic retinopathy classification is proposed in this paper, aiming to accurately classify the diabetic retinopathy stage to facilitate early diagnosis and intervention. To obtain the purpose of fact augmentation, the algorithm first enhances retinal lesion features using color correcting and multi-scale fusion algorithms. It then optimizes the local records via a multi-path multiplexing structure with convolutional kernels of exclusive sizes. Finally, a multi-feature fusion module is used to improve the accuracy of the diabetic retinopathy classification model. Two public datasets and a real hospital dataset are used to validate the algorithm. The accuracy is 98.9%, 99.3%, and 98.3%, respectively. The experimental results not only confirm the advancement and practicability of the algorithm in the field of automatic DR diagnosis, but also foretell its broad application prospects in clinical settings, which is expected to provide strong technical support for the early screening and treatment of diabetic retinopathy.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"365-381"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331241","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

Investigation of inert gas washout methods in a new numerical model based on an electrical analogy. 在基于电学类比的新数值模型中研究惰性气体冲洗方法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-07 DOI: 10.1007/s11517-024-03200-1

Christoph Schmidt, Wasilios Hatziklitiu, Frederik Trinkmann, Giorgio Cattaneo, Johannes Port

{"title":"Investigation of inert gas washout methods in a new numerical model based on an electrical analogy.","authors":"Christoph Schmidt, Wasilios Hatziklitiu, Frederik Trinkmann, Giorgio Cattaneo, Johannes Port","doi":"10.1007/s11517-024-03200-1","DOIUrl":"10.1007/s11517-024-03200-1","url":null,"abstract":"Inert gas washout methods have been shown to detect pathological changes in the small airways that occur in the early stages of obstructive lung diseases such as asthma and COPD. Numerical lung models support the analysis of characteristic washout curves, but are limited in their ability to simulate the complexity of lung anatomy over an appropriate time period. Therefore, the interpretation of patient-specific washout data remains a challenge. A new numerical lung model is presented in which electrical components describe the anatomical and physiological characteristics of the lung as well as gas-specific properties. To verify that the model is able to reproduce characteristic washout curves, the phase 3 slopes (S3) of helium washouts are simulated using simple asymmetric lung anatomies consisting of two parallel connected lung units with volume ratios of <math> <mfrac><mrow><mn>1.25</mn></mrow> <mrow><mn>0.75</mn></mrow> </mfrac> </math> , <math> <mfrac><mrow><mn>1.50</mn></mrow> <mrow><mn>0.50</mn></mrow> </mfrac> </math> , and <math> <mfrac><mrow><mn>1.75</mn></mrow> <mrow><mn>0.25</mn></mrow> </mfrac> </math> and a total volume flow of 250 ml/s which are evaluated for asymmetries in both the convection- and diffusion-dominated zone of the lung. The results show that the model is able to reproduce the S3 for helium and thus the processes underlying the washout methods, so that electrical components can be used to model these methods. This approach could form the basis of a hardware-based real-time simulator.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"447-466"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382181","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

A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk. 采用迭代优化策略的级联 FAS-UNet+ 框架，用于分割风险器官。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-04 DOI: 10.1007/s11517-024-03208-7

Hui Zhu, Shi Shu, Jianping Zhang

{"title":"A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk.","authors":"Hui Zhu, Shi Shu, Jianping Zhang","doi":"10.1007/s11517-024-03208-7","DOIUrl":"10.1007/s11517-024-03208-7","url":null,"abstract":"Segmentation of organs at risks (OARs) in the thorax plays a critical role in radiation therapy for lung and esophageal cancer. Although automatic segmentation of OARs has been extensively studied, it remains challenging due to the varying sizes and shapes of organs, as well as the low contrast between the target and background. This paper proposes a cascaded FAS-UNet+ framework, which integrates convolutional neural networks and nonlinear multi-grid theory to solve a modified Mumford-shah model for segmenting OARs. This framework is equipped with an enhanced iteration block, a coarse-to-fine multiscale architecture, an iterative optimization strategy, and a model ensemble technique. The enhanced iteration block aims to extract multiscale features, while the cascade module is used to refine coarse segmentation predictions. The iterative optimization strategy improves the network parameters to avoid unfavorable local minima. An efficient data augmentation method is also developed to train the network, which significantly improves its performance. During the prediction stage, a weighted ensemble technique combines predictions from multiple models to refine the final segmentation. The proposed cascaded FAS-UNet+ framework was evaluated on the SegTHOR dataset, and the results demonstrate significant improvements in Dice score and Hausdorff Distance (HD). The Dice scores were 95.22%, 95.68%, and HD values were 0.1024, and 0.1194 for the segmentations of the aorta and heart in the official unlabeled dataset, respectively. Our code and trained models are available at https://github.com/zhuhui100/C-FASUNet-plus .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"429-446"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373364","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

Cancer-on-chip: a breakthrough organ-on-a-chip technology in cancer cell modeling. 片上癌症：用于癌细胞建模的突破性片上器官技术。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-14 DOI: 10.1007/s11517-024-03199-5

Babak Nejati, Reza Shahhosseini, Mobasher Hajiabbasi, Nastaran Safavi Ardabili, Kosar Bagtashi Baktash, Vahid Alivirdiloo, Sadegh Moradi, Mohammadreza Farhadi Rad, Fatemeh Rahimi, Marzieh Ramezani Farani, Farhood Ghazi, Ahmad Mobed, Iraj Alipourfard

{"title":"Cancer-on-chip: a breakthrough organ-on-a-chip technology in cancer cell modeling.","authors":"Babak Nejati, Reza Shahhosseini, Mobasher Hajiabbasi, Nastaran Safavi Ardabili, Kosar Bagtashi Baktash, Vahid Alivirdiloo, Sadegh Moradi, Mohammadreza Farhadi Rad, Fatemeh Rahimi, Marzieh Ramezani Farani, Farhood Ghazi, Ahmad Mobed, Iraj Alipourfard","doi":"10.1007/s11517-024-03199-5","DOIUrl":"10.1007/s11517-024-03199-5","url":null,"abstract":"Cancer remains one of the leading causes of death worldwide. The unclear molecular mechanisms and complex in vivo microenvironment of tumors make it difficult to clarify the nature of cancer and develop effective treatments. Therefore, the development of new methods to effectively treat cancer is urgently needed and of great importance. Organ-on-a-chip (OoC) systems could be the breakthrough technology sought by the pharmaceutical industry to address ever-increasing research and development costs. The past decade has seen significant advances in the spatial modeling of cancer therapeutics related to OoC technology, improving physiological exposition criteria. This article aims to summarize the latest achievements and research results of cancer cell treatment simulated in a 3D microenvironment using OoC technology. To this end, we will first discuss the OoC system in detail and then demonstrate the latest findings of the cancer cell treatment study by Ooc and how this technique can potentially optimize better modeling of the tumor. The prospects of OoC systems in the treatment of cancer cells and their advantages and limitations are also among the other points discussed in this study.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"321-337"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479174","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

Digital transformation of mental health therapy by integrating digitalized cognitive behavioral therapy and eye movement desensitization and reprocessing. 通过整合数字化认知行为疗法和眼动脱敏与再处理疗法，实现心理健康疗法的数字化转型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-02-01 Epub Date: 2024-10-14 DOI: 10.1007/s11517-024-03209-6

Ju-Yu Wu, Ying-Ying Tsai, Yu-Jie Chen, Fan-Chi Hsiao, Ching-Han Hsu, Yen-Feng Lin, Lun-De Liao

{"title":"Digital transformation of mental health therapy by integrating digitalized cognitive behavioral therapy and eye movement desensitization and reprocessing.","authors":"Ju-Yu Wu, Ying-Ying Tsai, Yu-Jie Chen, Fan-Chi Hsiao, Ching-Han Hsu, Yen-Feng Lin, Lun-De Liao","doi":"10.1007/s11517-024-03209-6","DOIUrl":"10.1007/s11517-024-03209-6","url":null,"abstract":"Digital therapy has gained popularity in the mental health field because of its convenience and accessibility. One major benefit of digital therapy is its ability to address therapist shortages. Posttraumatic stress disorder (PTSD) is a debilitating mental health condition that can develop after an individual experiences or witnesses a traumatic event. Digital therapy is an important resource for individuals with PTSD who may not have access to traditional in-person therapy. Cognitive behavioral therapy (CBT) and eye movement desensitization and reprocessing (EMDR) are two evidence-based psychotherapies that have shown efficacy in treating PTSD. This paper examines the mechanisms and clinical symptoms of PTSD as well as the principles and applications of CBT and EMDR. Additionally, the potential of digital therapy, including internet-based CBT, video conferencing-based therapy, and exposure therapy using augmented and virtual reality, is explored. This paper also discusses the engineering techniques employed in digital psychotherapy, such as emotion detection models and text analysis, for assessing patients' emotional states. Furthermore, it addresses the challenges faced in digital therapy, including regulatory issues, hardware limitations, privacy and security concerns, and effectiveness considerations. Overall, this paper provides a comprehensive overview of the current state of digital psychotherapy for PTSD treatment and highlights the opportunities and challenges in this rapidly evolving field.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"339-354"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479178","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