Intensify3D: Normalizing signal intensity in large heterogenic image stacks

Three-dimensional structures in biological systems are routinely evaluated using large image stacks acquired from fluorescence microscopy; however, analysis of such data is muddled by variability in the signal across and between samples. Here, we present Intensify3D: a user-guided normalization algorithm tailored for overcoming common heterogeneities in large image stacks. We demonstrate the use of Intensify3D for analyzing cholinergic interneurons of adult murine brains in 2-Photon and Light-Sheet fluorescence microscopy, as well as of mammary gland and heart tissues. Beyond enhancement in 3D visualization in all samples tested, in 2-Photon in vivo images, this tool corrected errors in feature extraction of cortical interneurons; and in Light-Sheet microscopy, it enabled identification of individual cortical barrel fields and quantification of somata in cleared adult brains. Furthermore, Intensify3D enhanced the ability to separate signal from noise. Overall, the universal applicability of our method can facilitate detection and quantification of 3D structures and may add value to a wide range of imaging experiments.