论文标题
非侵入性脑刺激的个性化深脑结构的端到端语义分割
End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation
论文作者
论文摘要
深脑区域的电刺激或调节通常用于治疗多种神经系统疾病的临床程序中。特别是，经颅直流刺激（TDC）被广泛用作负担得起的临床应用，该应用是通过连接到头皮上的电极应用的。但是，由于解剖学复杂性和高主体间变异性，很难确定不同大脑区域中电场（EF）的数量和分布。个性化的TDC是一种新兴的临床程序，用于耐电极蒙太奇以进行准确的靶向。该过程由从解剖图像（例如MRI）产生的计算头模型来指导。可以通过模拟研究来计算EF分段模型中EF的分布。因此，对不同大脑结构的快速，准确和可行的分割将为定制的TDCS研究提供更好的调整。在这项研究中，提出了一个单个编码器多代码卷积神经网络，以进行深脑分割。该提出的结构经过训练，可以使用T1加权MRI进行七个深脑结构。将网络生成的模型与使用半自动方法构建的参考模型进行了比较，并且它具有较高的匹配，尤其是在丘脑（DICE系数（DC）= 94.70％），尾状（DC = 91.98％）和putamen（DC = 90.31％）结构。 TDC在生成和参考模型中的电场分布相互匹配，这表明其在临床实践中的潜在有用性。
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.
