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Generation of head models for brain stimulation using deep convolution networks

Abstract: 

Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer’s disease and Parkinson’s disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters’ optimizations. A major factor of brain induced electric field is the inter-subject variability, therefore a computer simulation is frequently used to simulate different TMS setups using anatomical models generated from MR images of the examined subject. Human head models are generated by segmentation of MR images into different anatomical tissues with a uniform electric conductivity value for each tissue. This process is time-consuming and requires a special experience to segment a relatively large number of tissues.

In this paper, we propose a deep convolution network for human head segmentation that is convenient for simulation of electrical field distribution, such as TMS. The proposed network is used to generate head models and is evaluated using TMS simulation studies. Results indicate that the head models generated using the proposed network demonstrate strong matching results with those achieved from manually segmented ones.

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Paper Details

Authors:
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata
Submitted On:
18 September 2019 - 12:31am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Essam A. Rashed
Paper Code:
2190
Document Year:
2019
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Document Files

ICIP2019_poster_2190.pdf

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[1] Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, "Generation of head models for brain stimulation using deep convolution networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4663. Accessed: Oct. 18, 2019.
@article{4663-19,
url = {http://sigport.org/4663},
author = {Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata },
publisher = {IEEE SigPort},
title = {Generation of head models for brain stimulation using deep convolution networks},
year = {2019} }
TY - EJOUR
T1 - Generation of head models for brain stimulation using deep convolution networks
AU - Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4663
ER -
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). Generation of head models for brain stimulation using deep convolution networks. IEEE SigPort. http://sigport.org/4663
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, 2019. Generation of head models for brain stimulation using deep convolution networks. Available at: http://sigport.org/4663.
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). "Generation of head models for brain stimulation using deep convolution networks." Web.
1. Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. Generation of head models for brain stimulation using deep convolution networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4663