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

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 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.

arXiv commentTo appear in Neural Networks

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
• Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
• DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
• Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features
• Development of accurate human head models for personalized electromagnetic dosimetry using deep learning

Cited by in corpus (4)

• Review on Biophysical Modelling and Simulation Studies for Transcranial Magnetic Stimulation
• Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES
• DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains
• Factorisation-based Image Labelling