利用深度学习在sem成像的胎盘中自动标记成纤维细胞和内皮细胞

Bioimaging (Bristol. Print) Pub Date : 2020-02-26 DOI:10.5220/0008949700460053

Benita Scout Mackay, Sophie Blundell, O. Etter, Yunhui Xie, M. McDonnel, M. Praeger, J. Grant-Jacob, R. Eason, Rohan M. Lewis, B. Mills

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引用次数: 4

摘要

分析胎盘内的成纤维细胞是研究胎盘生长因子的必要条件，胎盘生长因子与终身健康和慢性疾病风险有关。由于成纤维细胞结构的变化和复杂性，其二维分析具有挑战性。3D成像可以提供重要的可视化效果，但由于需要大量的人工处理，生成的图像是极其劳动密集型的。机器学习可用于自动化标签过程，以实现更快的3D分析。在这里，一个深度神经网络被训练来标记来自连续块面扫描电子显微镜(SBFSEM)胎盘成像的成纤维细胞。

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Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning

Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.

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Bioimaging (Bristol. Print)

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