Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment single neurons based on branch-specific structural features. With high quality brain images available nowadays, NeuroRetriever allows us to successfully retrieve 28,125 single-neuron images from 22,037 raw brain images validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

中文翻译：

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NeuroRetriever：用于连接组组装的自动神经元分割

从大量嘈杂的原始图像中分割单个神经元是构建神经元到神经元连接的综合图以预测大脑中信息流的第一步。数以千计的荧光标记的大脑神经元已被成像。然而，绘制完整的连接组仍然具有挑战性，因为成像的神经元经常纠缠在一起，并且手动分割大量单个神经元既费力又容易产生偏差。在这项研究中，我们报告了一种自动算法 NeuroRetriever，用于对成年果蝇大脑中单个神经元的共聚焦荧光图像进行无偏大规模分割。NeuroRetriever 使用高动态范围阈值方法根据分支特定的结构特征对单个神经元进行分割。现在有了高质量的大脑图像，NeuroRetriever 使我们能够从 22,037 幅经人类分割验证的原始大脑图像中成功检索 28,125 张单神经元图像。因此，自动化的 NeuroRetriever 将大大加速构建完整连接组的单个神经元的 3D 重建。