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Abstract #67494 Published in IGR 17-4
An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells
Dordea AC; Bray MA; Allen K; Logan DJ; Fei F; Malhotra R; Gregory MS; Carpenter AE; Buys ESExperimental Eye Research 2016; 147: 50-56
A fully automated and robust method was developed to quantify β-III-tubulin-stained retinal ganglion cells, combining computational recognition of individual cells by CellProfiler and a machine-learning tool to teach phenotypic classification of the retinal ganglion cells by CellProfiler Analyst. In animal models of glaucoma, quantification of immunolabeled retinal ganglion cells is currently performed manually and remains time-consuming. Using this automated method, quantifications of retinal ganglion cell images were accelerated tenfold: 1800 images were counted in 3 h using our automated method, while manual counting of the same images took 72 h. This new method was validated in an established murine model of microbead-induced optic neuropathy. The use of the publicly available software and the method's user-friendly design allows this technique to be easily implemented in any laboratory.
Anesthesia Center for Critical Care Research, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital Research Institute, Harvard Medical School, Boston, MA, USA.
Full articleClassification:
3.13.3 RGC Imaging (Part of: 3 Laboratory methods > 3.13 In vivo imaging)
5.1 Rodent (Part of: 5 Experimental glaucoma; animal models)