Max Planck Institute of Microstructure Physics, Weinberg 2, Halle D-06120, Germany; Max Planck-University of Toronto Centre for Neural Science and Technology, Canada.
Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, Ontario M5S 3G4, Canada.
J Neurosci Methods. 2024 Nov;411:110273. doi: 10.1016/j.jneumeth.2024.110273. Epub 2024 Aug 27.
The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals.
We have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images.
NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations.
NeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution.
We offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation.
在神经元网络的显微镜图像中对细胞和神经突进行分割，可以提供有关神经元生长和神经元分化的有价值的定量信息，包括细胞数量、神经突数量、神经突长度和神经突方向。这些信息对于评估神经元网络对外界刺激的反应非常重要，对于研究神经元结构非常有用，例如研究神经退行性疾病和药物。
我们开发了一种开源软件 NeuroQuantify，它使用深度学习技术来高效、快速地分割相差显微镜图像中的细胞和神经突。
NeuroQuantify 具有几个关键功能：（i）自动检测细胞和神经突；（ii）基于相差显微镜图像分割的图像后处理，用于定量神经突长度测量；（iii）神经突方向的识别。
NeuroQuantify 克服了现有方法在自动和准确分析神经元结构方面的一些局限性。它是为相差图像而不是荧光图像开发的。除了典型的细胞计数功能外，NeuroQuantify 还可以检测和计数神经突，测量神经突长度，并生成神经突方向分布。
我们提供了一种快速有效地评估网络发展的有价值的工具。用户友好的 NeuroQuantify 软件可以从 GitHub 上安装并免费下载，网址为 https://github.com/StanleyZ0528/neural-image-segmentation。
