  In several diagnosis and therapy procedures based on electrostimulation
effect, the internal physical quantity related to the stimulation is the
induced electric field. To estimate the induced electric field in an individual
human model, the segmentation of anatomical imaging, such as (magnetic
resonance image (MRI) scans, of the corresponding body parts into tissues is
required. Then, electrical properties associated with different annotated
tissues are assigned to the digital model to generate a volume conductor. An
open question is how segmentation accuracy of different tissues would influence
the distribution of the induced electric field. In this study, we applied
parametric segmentation of different tissues to exploit the segmentation of
available MRI to generate different quality of head models using deep learning
neural network architecture, named ForkNet. Then, the induced electric field
are compared to assess the effect of model segmentation variations.
Computational results indicate that the influence of segmentation error is
tissue-dependent. 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). A CSF segmentation accuracy reduction of 10% in
terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced
electric field in both applications. However, a GM segmentation accuracy
reduction of 5.6% DC leads to increase of normalized induced electric field up
to 6%. Opposite trend of electric field variation was found between CSF and GM
for both TMS and tES. The finding obtained here would be useful to quantify
potential uncertainty of computational results.
