College of Electrical and Information Engineering, Hunan University, Hunan, China.
National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha, China.
Methods Mol Biol. 2024;2831:179-197. doi: 10.1007/978-1-0716-3969-6_12.
Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.
从 3D 神经元显微镜图像中对神经元结构进行数字化重建对于定量研究脑回路和功能至关重要。目前，神经元重建主要通过手动或半自动方法获得。然而，这些方法工作量大，特别是在处理整个大脑显微镜成像数据的巨大体积时。在这里，我们提出了一个基于深度学习的神经元形态分析工具箱（DNeuroMAT），用于自动分析神经元显微镜图像，它由三个模块组成：神经元分割、神经元重建和神经元关键点检测。
