Integrated deep learning model for personalized transcranial magnetic stimulation

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In this year, we have developed a new deep learning model for the segmentation of all head tissues using multi-modal MRI scans. The developed model segmented MRI scans into 16 different tissues. A dataset of multi-modal MRI (T1w, T2w, PD and MRA) are collected from 600 subjects. The images are pre-processed through bias-correction, registration and normalization to be used for human head dataset. A segmentation of 20 subjects are obtained through semi-automatic method and used to train the deep learning model. The developed method is applied to the remaining MRI dataset to generate segmented head models. After visual validation, a set of 196 fully segmented human head models with variabilities in gender and age was approved. The use of MRA leads to significant improvement of identification of brain vessels and arteries. This dataset will be used next year for large-scale TMS study. Initial TMS study was conducted using two subjects (through collaborators) to compute the induced electric field using different coil positions, orientation and location around motor cortex.Another deep learning model was developed for the estimation of the induced electric field in human brain directly from the anatomical images. Training of the new model is scheduled for next year.