دانلود مقاله انگلیسی رایگان:توسعه مدل های دقیق سر انسان برای دوزیمتری الکترومغناطیسی شخصی با استفاده از یادگیری عمیق - 2019
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  • Development of accurate human head models for personalized electromagnetic dosimetry using deep learning Development of accurate human head models for personalized electromagnetic dosimetry using deep learning
    Development of accurate human head models for personalized electromagnetic dosimetry using deep learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Development of accurate human head models for personalized electromagnetic dosimetry using deep learning


    ترجمه فارسی عنوان مقاله:

    توسعه مدل های دقیق سر انسان برای دوزیمتری الکترومغناطیسی شخصی با استفاده از یادگیری عمیق


    منبع:

    Sciencedirect - Elsevier - NeuroImage, 202 (2019) 116132: doi:10:1016/j:neuroimage:2019:116132


    نویسنده:

    Essam A. Rashed a,b,c,*, Jose Gomez-Tames a, Akimasa Hirata a,d


    چکیده انگلیسی:

    The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly generated via the segmentation of magnetic resonance images into different anatomical tissues. This process is time consuming and requires special experience for segmenting a relatively large number of tissues. Thus, it is challenging to accurately compute the electric field in different specific brain regions. Recently, deep learning has been applied for the segmentation of the human brain. However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry. In this study, we propose a new architecture for a convolutional neural network, named ForkNet, to perform the segmentation of whole human head structures, which is essential for evaluating the electrical field distribution in the brain. The proposed network can be used to generate personalized head models and applied for the evaluation of the electric field in the brain during transcranial magnetic stimulation. Our computational results indicate that the head models generated using the proposed network exhibit strong matching with those created via manual segmentation in an intra-scanner segmentation task.
    Keywords: convolutional neural network | Deep learning | Image segmentation | Transcranial magnetic stimulation


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 16
    حجم فایل: 6837 کیلوبایت

    قیمت: رایگان


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