Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.

中文翻译：

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用于整个小鼠大脑图像数据集中的3D细胞检测的深度学习算法

要了解神经系统的功能，就必须绘制其功能，解剖结构或基因表达所定义的组成细胞的空间分布。最近，组织制备和显微镜技术的发展使整个啮齿动物大脑的细胞群体成像。但是，手动映射这些神经元容易产生偏差，并且通常不切实际地耗时。在这里，我们介绍了使用标准台式计算机硬件对小鼠全脑显微镜图像中的神经元体细胞进行3D全自动检测的开源算法。我们通过绘制大细胞群体的全脑位置图来证明我们方法的适用性和力量，这些细胞标记有通过逆转录跨突触病毒感染表达的细胞质荧光蛋白。