Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast and accurate active neuron segmentation is critical when processing these videos. Here we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets acquired by independent experimental groups; the difference in accuracy was enlarged when processing datasets containing few manually marked ground truths. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.

荧光基因编码的钙指示剂和双光子显微镜通过在多种动物模型中生成大规模的体内记录来帮助了解大脑功能。处理这些视频时，自动、快速和准确的主动神经元分割至关重要。在这里，我们开发并描述了一种新方法，即浅 U-Net 神经元分割 (SUNS)，可以快速准确地从双光子荧光成像视频中分割出活跃的神经元。我们使用时间过滤和白化方案来提取与活动神经元相关的时间特征，并使用紧凑的浅 U-Net 来提取神经元的空间特征。在处理独立实验组获得的多个数据集时，我们的方法比最先进的技术更准确，速度也快一个数量级；在处理包含很少手动标记的基本事实的数据集时，精度差异会扩大。我们还开发了一个在线版本，有可能实现实时反馈神经科学实验。