End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

Neural Networks : the Official Journal of the International Neural Network Society
Essam A RashedAkimasa Hirata

Abstract

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 mode...Continue Reading

Citations

Jul 9, 2020·Physics in Medicine and Biology·Jose Gomez-TamesAkimasa Hirata
Feb 2, 2021·Physics in Medicine and Biology·Essam A RashedAkimasa Hirata
Mar 2, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Chengli PengJiayi Ma

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