Human Superficial White Matter 3D multibeam serial electron microscopy data and segmentation
收藏DataCite Commons2025-04-22 更新2025-05-07 收录
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https://figshare.com/articles/dataset/Human_Superficial_White_Matter_3D_multibeam_serial_electron_microscopy_data_and_segmentation/28838549/1
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Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm<sup>3</sup> resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm, leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2,102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.
位于浅表白质的短程联络纤维,在介导人类高阶认知功能中发挥着关键作用。在微观层面对短程联络纤维开展精细化形态学表征,有望为解析人类大脑皮层-皮层连接的轴突特征提供重要见解,但此前受限于大组织体积下的纳米级分辨率成像难题,这一目标迄今尚未实现。本研究借助自动化分析方法,报道了基于多束扫描电子显微镜(multi-beam scanning electron microscopy,EM)数据集的分析结果,该数据集以4×4×33 nm³的分辨率采集自一块尺寸为200×200×112 μm的人类浅表白质组织样本。本研究借助迁移学习与Dropout正则化技术辅助,利用深度卷积神经网络(deep convolutional neural networks,CNNs)实现了髓鞘与有髓轴突的自动化分割。本次共分割得到128285条有髓轴突,其中分别有70321条、2102条轴突长度超过10 μm与100 μm。研究观察到,单条轴突在其全长范围内存在显著的直径局部变异(即轴突串珠样改变)与方向偏移(即轴突波纹状起伏)。长度超过10 μm的有髓轴突,其内径约为0.5 μm,外径约为1 μm,g比率(g-ratio)约为0.5。本研究填补了浅表白质轴突形态计量学研究的知识空白,并为多领域后续的各类研究提供了大型三维人类电子显微镜数据集与精准的分割结果。
提供机构:
figshare
创建时间:
2025-04-22



