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

• URL: http://arxiv.org/abs/2002.09080v1
• Date: Fri, 21 Feb 2020 01:21:34 GMT
• Title: Development of accurate human head models for personalized electromagnetic dosimetry using deep learning
• Authors: Essam A. Rashed and Jose Gomez-Tames and Akimasa Hirata
• Abstract summary: We propose a new architecture for a convolutional neural network, named ForkNet, to perform the segmentation of whole human head structures. 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.
• Score: 2.750124853532831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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