神经元形态的自动重建:概述

Duncan E. Donohue, Giorgio A. Ascoli
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引用次数: 149

摘要

神经形态学的数字重建是研究神经系统的一项强有力的技术。这一过程包括通过光学显微镜将神经元的轴突和树突成像成适合定量分析和计算建模的几何格式。神经追踪的算法自动化有望提高形态学重建的速度、准确性和可重复性。随着细胞成像的最新突破和光学显微镜的加速发展,神经元形态的自动重建将在高通量筛选和连接组数据获取的发展中发挥核心作用。然而,尽管图像处理算法不断进步,迄今为止,人工跟踪仍然是数字化神经元形态的压倒性选择。我们总结了自动化重建中涉及的问题,概述了可用的技术,并提供了对未来前景的现实评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Automated reconstruction of neuronal morphology: An overview

Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

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Brain Research Reviews
Brain Research Reviews 医学-神经科学
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