  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. However, automatic and accurate
analysis of neuronal structures from phase contrast images has remained
challenging. To address this, 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. The user-friendly NeuroQuantify software can be installed
and freely downloaded from GitHub
https://github.com/StanleyZ0528/neural-image-segmentation.
