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In several diagnosis and therapy procedures based on electrostimulationeffect, the internal physical quantity related to the stimulation is theinduced electric field. To estimate the induced electric field in an individualhuman model, the segmentation of anatomical imaging, such as (magneticresonance image (MRI) scans, of the corresponding body parts into tissues isrequired. Then, electrical properties associated with different annotatedtissues are assigned to the digital model to generate a volume conductor. Anopen question is how segmentation accuracy of different tissues would influencethe distribution of the induced electric field. In this study, we appliedparametric segmentation of different tissues to exploit the segmentation ofavailable MRI to generate different quality of head models using deep learningneural network architecture, named ForkNet. Then, the induced electric fieldare compared to assess the effect of model segmentation variations.Computational results indicate that the influence of segmentation error istissue-dependent. In brain, sensitivity to segmentation accuracy is relativelyhigh in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low inwhite matter for transcranial magnetic stimulation (TMS) and transcranialelectrical stimulation (tES). A CSF segmentation accuracy reduction of 10% interms of Dice coefficient (DC) lead to decrease up to 4% in normalized inducedelectric field in both applications. However, a GM segmentation accuracyreduction of 5.6% DC leads to increase of normalized induced electric field upto 6%. Opposite trend of electric field variation was found between CSF and GMfor both TMS and tES. The finding obtained here would be useful to quantifypotential uncertainty of computational results.

Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES