2018 9th Cairo International Biomedical Engineering Conference (CIBEC)最新文献_第5页

Prediction of Heat Generation and Tissue Thermal Diffusivity During Laser Hair Removal 激光脱毛过程中产热及组织热扩散率的预测

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641802

H. Kassahun, Amanuel Shigut Dinsa, Henok Tadesse Moges, W. Negussie, Okebiorun Michael Oluwaseyi, M. Rushdi

{"title":"Prediction of Heat Generation and Tissue Thermal Diffusivity During Laser Hair Removal","authors":"H. Kassahun, Amanuel Shigut Dinsa, Henok Tadesse Moges, W. Negussie, Okebiorun Michael Oluwaseyi, M. Rushdi","doi":"10.1109/CIBEC.2018.8641802","DOIUrl":"https://doi.org/10.1109/CIBEC.2018.8641802","url":null,"abstract":"During laser hair removal, monitoring the temperature field of hair follicles is needed to ensure patient safety. To determine this temperature field at any time, parameters such as heat energy and thermal diffusivity of tissue should be obtained. The aim of this paper is to apply numerical optimization schemes for the estimation of these parameters during laser hair removal. Levenberg-Marquardt and Gauss-Newton methods were applied to estimate the parameters. Once these parameters are found, the temperature field at any time can easily be determined by numerically solving the 2D heat diffusion equation. The estimation methods were tested with random initial values, larger and smaller than the target true value. Results showed that these algorithms are accurate to estimate the target unknown parameters. The temperature distribution obtained by using these predicted parameters could help dermatologists during hair removal procedures. Moreover, the prediction of required heat energy could aid clinicians to select a laser source with appropriate wavelength and pulse width.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114853115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Logic Approach For Medical Equipment Supplier Evaluation and Selection 医疗设备供应商评价与选择的模糊逻辑方法

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641810

Mohamed E. Ragheb, M. A. Hassan, W. Al-Atabany, A. Seddik, M. El-Wakad

引用次数: 2

Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons 脊髓运动神经元的自动细胞类型分类和死亡检测

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641824

Mai Gamal, M. Mousa, S. Eldawlatly, Sherif M. Elbasiouny

引用次数: 0

Automatic Segmentation of the Left Ventricle Cavity from Cine MRI Images 从MRI影像中自动分割左心室腔

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-12-01 DOI: 10.1109/CIBEC.2018.8641836

Marwa M. A. Hadhoud

引用次数: 0

Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks 基于深度卷积神经网络的结构脑磁共振图像质量自动评价

2018 9th Cairo International Biomedical Engineering Conference (CIBEC) Pub Date : 2018-11-21 DOI: 10.29007/J68T

Sheeba J. Sujit, R. Gabr, Ivan Coronado, M. Robinson, S. Datta, P. Narayana

{"title":"Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks","authors":"Sheeba J. Sujit, R. Gabr, Ivan Coronado, M. Robinson, S. Datta, P. Narayana","doi":"10.29007/J68T","DOIUrl":"https://doi.org/10.29007/J68T","url":null,"abstract":"Automated evaluation of image quality is essential to assure accurate diagnosis and effective patient management. This is particularly important for multi-center studies, typically employed in clinical trials, in which the data are acquired on different machines with different protocols. Visual quality assessment of magnetic resonance imaging (MRI) data is subjective and impractical for large datasets. Data-intensive deep learning methods such as convolutional neural networks (CNNs) are promising tools for processing large-scale imaging datasets for automated quality assessment. In this study, we evaluate a CNN-based method for quality assessment of the Autism Brain Imaging Data Exchange (ABIDE) structural brain MRI dataset acquired from 17 sites on more than a thousand subjects. The CNN architecture consisted of an input layer, four convolution layers, two fully connected layers, and an output layer. A balanced set of 348 image volumes was used in the study. 60% of the data was used for training, 15% for validation, and 25% for testing. The results of the automated approach were compared with the evaluation by the radiologist. Performance of the CNN was assessed using the confusion matrix. The concordance in image quality labels between the expert and CNN was 86% (sensitivity = 81%, specificity = 92%). The present study shows that the proposed model can evaluate the image quality of brain MRI with higher classification accuracy compared to previous state-of-the-art classical machine learning algorithms.","PeriodicalId":407809,"journal":{"name":"2018 9th Cairo International Biomedical Engineering Conference (CIBEC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117092959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12