Eric Hwang Lab

Recent advancements in automated fluorescence microscopy has made high-content screening an essential technique for discovering novel molecular pathways in disease or potential new therapeutic treatments. Automatic morphology quantification from images of fluorescence microscopy plays an increasingly important role in high-content screens. However, there are very few freeware tools or methods which provide automatic neuronal morphology quantification for these high-content screens. To automate these morphological feature measurements, we have developed NeurphologyJ; it is capable of automatically quantifying neuronal morphology such as soma number and size, neurite length, neurite ending points and attachment points. NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image-processing and analysis platform.

NeurphologyJ has been accepted by the journal BMC Bioinformatics and can be found here.