In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as (magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

翻译：在基于电动刺激效应的若干诊断和治疗程序中,与刺激有关的内部物理数量是导电场。为了估算单个人类模型中诱导电场,需要将解剖成像进行分解,例如(磁共振图像(MRI)扫描),对组织中相应的身体部分进行分解。然后,与不同附加说明组织相关的电气特性被指派给数字模型,以生成一个体积导体。一个未决问题是,不同组织的分解精度如何影响诱导电场的分布。在本研究中,我们应用不同组织的分解等分解,以利用现有磁共振成像的分解趋势,利用深学习神经网络结构(名为ForkNet),生成不同质量的解剖成成成成像。然后,引致电离分解错误的影响是取决于组织导体导导导体。在大脑中,对分解精度准确度的敏感度在灰质(GM)和白体中的分解分解分解度值较低,在电流变电流中,在磁感测中,递解导分解为递解的递解分解中,递解为递解为递减为递减为递解分解分解分解分解为C。