Automated cell detection for immediate early gene-expressing neurons using inhomogeneous background subtraction in fluorescent images

Although many methods for automated fluorescent-labeled cell detection have been proposed, not all of them assume a highly inhomogeneous background arising from complex biological structures. Here, we propose an automated cell detection algorithm that accounts for and subtracts the inhomogeneous background by avoiding high-intensity pixels in the blur filtering calculation. Cells were detected by intensity thresholding in the background-subtracted image, and the algorithms performance was tested on NeuN- and c-Fos-stained images in the mouse prefrontal cortex and hippocampal dentate gyrus. In addition, applications in c-Fos positive cell counting and the quantification for the expression level in double-labeled cells were demonstrated. Our method of automated detection after background assumption (ADABA) offers the advantage of high-throughput and unbiased analysis in regions with complex biological structures that produce inhomogeneous background. Highlights- We proposed a method to assume and subtract inhomogeneous background pattern. (79/85) - Cells were automatically detected in the background-subtracted image. (71/85) - The automated detection results corresponded with the manual detection. (73/85) - Detection of IEG positive cells and overlapping with neural marker were demonstrated. (85/85)