Integrated deep learning model for personalized transcranial magnetic stimulation

今年，我们开发了一种新的深度学习模型，用于使用多模态MRI扫描对所有头部组织进行分割。开发的模型将MRI扫描分割成16个不同的组织。收集600名受试者的多模态MRI数据集（T1w、T2w、PD和MRA）。图像经过偏置校正、配准和归一化等预处理，用于人类头部数据集。通过半自动方法得到20个主题的分割，并用于训练深度学习模型。将该方法应用于剩余的MRI数据集，生成分割的头部模型。经过视觉验证后，196个具有性别和年龄变异的完全分割的人类头部模型被批准。MRA的使用显著提高了对脑血管和动脉的识别。该数据集将于明年用于大规模的经颅磁刺激研究。最初的TMS研究由两名受试者（通过合作者）进行，计算运动皮层周围不同线圈位置、方向和位置的感应电场。建立了另一种深度学习模型，用于直接从解剖图像中估计人脑感应电场。新型号的培训计划在明年进行。

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.