DeadEasy neurons: Automatic counting of HB9 neuronal nuclei in Drosophila

Abstract

Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities. '

OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects

“…A qualitative assessment of robustness was undertaken by analysing pictures containing artefacts (A). Representative portions of 1.7cm by 1.7cm (200×200 pixels) illustrate the results of the presence of bubbles (1), cracks in the agar (2), dust (3) and edge of dish (4) in the region of interest. Objects detected by OpenCFU, NICE and the ImageJ macro (IJM) are represented by ellipses, crosses and arbitrary colours, respectively.…”

Automatic cell counting in vivo in the larval nervous system of Drosophila

“…The image‐processing method we have developed – and which name DeadEasy larval glia – identifies cells, and produces a stack of images with the same number of images as the original raw stack, where the identified cells are reproduced in corresponding locations. To validate the technique, we generated a merged stack of images, combining the original raw stack with the processed stack of identified objects (as previously done for similar methods (Forero et al , 2010b; Fig. 8A).…”

CAS: Cell Annotation Software – Research on Neuronal Tissue Has Never Been so Transparent

“…Researchers, who specialize in image processing, have also created solutions dedicated to specific applications. Examples include the ImageJ plug-in (Forero et al 2010 ; Pool et al 2008 ), an ImageJ framework expansion (Gulyás et al 2016 ), software for the Matlab environment (La Torre et al 2013 ), and stand-alone applications such as CellProfiler (Kamentsky et al 2011 ; Carpenter et al 2006 ). Commercial solutions also exist, but their license usually precludes them from comparison.…”