提取神经突结构用于高通量成像筛选基于神经元的检测

Yong Zhang, Xiaobo Zhou, Stephen T. C. Wong
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引用次数: 2

摘要

神经元图像分析最近成为定量系统神经生物学和高通量药物筛选的关键组成部分。本文提出了一种快速自动提取显微神经元图像中神经突结构的新算法。该算法基于体细胞分割、种子点检测、递归中心线检测和二维曲线平滑等新方法。该算法是完全自动的,不需要任何人工干预,同时对于处理低对比度或低信噪比等质量较差的图像具有足够的鲁棒性。它可以用于精确提取高度复杂的神经突结构。所有这些优点使得该算法适用于系统生物学和药物筛选中日益苛刻和复杂的图像分析任务
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extraction of Neurite Structures for High Throughput Imaging Screening of Neuron Based Assays
Neuron image analysis has recently emerged as a critical component for enabling quantitative systems neurobiology and high throughput drug screening. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction while robust enough for processing images of poor quality, e.g., low contrast or low signal-to-noise ratio. It can be used to extract accurately highly complex neurite structures. All these advantages make the proposed algorithm suitable for increasingly demanding and complex image analysis tasks in systems biology and drug screening
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