SimpylCellCounter: An Automated Solution for Quantifying Cells in Brain Tissue

Abstract

Rationale & ObjectiveManual quantification of activated cells can provide valuable information about stimuli-induced changes within brain regions; however, this analysis remains time intensive. Therefore, we created SimpylCellCounter (SCC), an automated method to quantify cells that express Cfos protein, an index of neuronal activity, in brain tissue and benchmarked it against two widely-used methods: OpenColonyFormingUnit (OCFU) and ImageJ Edge Detection Macro (IMJM). MethodsIn Experiment 1, manually-obtained counts were compared to those detected via OCFU, IMJM and SCC. The absolute error in counts (manual versus automated method) was calculated, and error types were categorized as false positives or negatives. In Experiment 2, performance analytics of OCFU, IMJM and SCC were compared. In Experiment 3, SCC performed analysis on images it was not trained on, to assess its general utility. Results & ConclusionsWe found SCC to be highly accurate and efficient in quantifying both cells with circular morphologies and those expressing Cfos. Additionally, SCC utilizes a new approach for counting overlapping cells with a pretrained convolutional neural network classifier. The current study demonstrates that SCC is a novel, automated tool to quantify cells in brain tissue, complementing current, open-sourced quantification methods designed to detect cells in vitro.