Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies. Segmentation is based on identifying different anatomical structures through labeling different tissues through medical imaging modalities. The segmentation of brain structures is commonly feasible with several remarkable contributions mainly for medical perspective; however, non-brain tissues are of less interest due to anatomical complexity and difficulties to be observed using standard medical imaging protocols. The lack of whole head segmentation methods and unavailability of large human head segmented datasets limiting the variability studies, especially in the computational evaluation of electrical brain stimulation (neuromodulation), human protection from electromagnetic field, and electroencephalography where non-brain tissues are of great importance. To fill this gap, this study provides an open-access Segmented Head Anatomical Reference Models (SHARM) that consists of 196 subjects. These models are segmented into 15 different tissues; skin, fat, muscle, skull cancellous bone, skull cortical bone, brain white matter, brain gray matter, cerebellum white matter, cerebellum gray matter, cerebrospinal fluid, dura, vitreous humor, lens, mucous tissue and blood vessels. The segmented head models are generated using open-access IXI MRI dataset through convolutional neural network structure named ForkNet+. Results indicate a high consistency in statistical characteristics of different tissue distribution in age scale with real measurements. SHARM is expected to be a useful benchmark not only for electromagnetic dosimetry studies but also for different human head segmentation applications.

翻译：摘要：人体头部解剖组织的可靠分割是脑部映射、手术规划及相关计算仿真研究等多项临床应用的关键步骤。该分割过程通过医学成像模态对组织进行标记，从而识别不同的解剖结构。尽管脑部结构的分割在医学领域已有多项显著成果且普遍可行，但非脑组织由于解剖复杂性及标准医学成像协议难以观测的问题，研究关注度较低。目前缺乏全头部分割方法及大规模人头部分割数据集，限制了变异性研究，尤其是在对非脑组织至关重要的电刺激（神经调控）计算评估、人体电磁场防护及脑电图领域。为填补这一空白，本研究提供了包含196名受试者的开源分段式头部解剖参考模型（SHARM）。这些模型被分割为15种不同组织：皮肤、脂肪、肌肉、颅骨松质骨、颅骨皮质骨、脑白质、脑灰质、小脑白质、小脑灰质、脑脊液、硬脑膜、玻璃体、晶状体、黏膜组织及血管。分割头部模型基于开源IXI MRI数据集，通过卷积神经网络结构ForkNet+生成。结果表明，不同组织分布的统计特征与实际测量数据在年龄尺度上具有高度一致性。SHARM不仅有望成为电磁剂量学研究的重要基准，还可广泛应用于各类人类头部分割任务。