  Electro-stimulation or modulation of deep brain regions is commonly used in
clinical procedures for the treatment of several nervous system disorders. In
particular, transcranial direct current stimulation (tDCS) is widely used as an
affordable clinical application that is applied through electrodes attached to
the scalp. 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. Personalized tDCS is an emerging
clinical procedure that is used to tolerate electrode montage for accurate
targeting. This procedure is guided by computational head models generated from
anatomical images such as MRI. Distribution of the EF in segmented head models
can be calculated through simulation studies. Therefore, fast, accurate, and
feasible segmentation of different brain structures would lead to a better
adjustment for customized tDCS studies. In this study, a single-encoder
multi-decoders convolutional neural network is proposed for deep brain
segmentation. The proposed architecture is trained to segment seven deep brain
structures using T1-weighted MRI. Network generated models are compared with a
reference model constructed using a semi-automatic method, and it presents a
high matching especially in Thalamus (Dice Coefficient (DC) = 94.70%), Caudate
(DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution
during tDCS in generated and reference models matched well each other,
suggesting its potential usefulness in clinical practice.
