Search Results for author:
Found 4 papers, 3 papers with code
Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES
In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES).
Development of accurate human head models for personalized electromagnetic dosimetry using deep learning
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.
End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation
However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability.
Deep learning-based development of personalized human head model with non-uniform conductivity for brain stimulation
This paper proposes a novel approach to the automatic estimation of electric conductivity in the human head for volume conductor models without anatomical segmentation.
