![]() Specialized image processing tools for brain datasets have been designed to correct signal homogeneity but are limited to a specific use (e.g. However, they do not account for differences that arise from sample specific factors and are sub-optimal when signal-to-noise ratios, imaging conditions, and pixel distributions vary in a location-dependent manner – a typical property of 3D imaging. Standard filtering as well as total image correction tools that construct a mathematical model based on multiple single plane images 5, 6, 7 may excel at improving specific types of shading or microscopy distortions. Such heterogeneity is exacerbated the larger the imaged structure and it often limits the ability to perform downstream applications such as feature extraction, threshold-based detection, co-localization, three dimensional (3D) rendering, and image stitching. These elements combined with imaging distortions and illumination gradients contribute to non-uniformity both within and across image stacks and may lead to erroneous conclusions. excessive blood vessel absorbance in live imaging, or non-uniform tissue clearing/antibody penetration in fixed tissues). Signal heterogeneity often arises from sample-specific factors (e.g. Hence, much like biochemical and molecular experimental datasets 2, 3, accurate normalization, beyond background subtraction 4 of imaging signals, could reduce tissue-derived and/or technical variation. Nowadays, many imaging experiments encompass some form of depth or a Z-stack of images, often from distinct regions in the sample. 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.įluorescence microscopy once relied on single plane images from relatively small areas, and yielded limited amounts of quantitative data 1. Furthermore, Intensif圓D enhanced the ability to separate signal from noise. 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. We demonstrate the use of Intensif圓D 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. Here, we present Intensif圓D: a user-guided normalization algorithm tailored for overcoming common heterogeneities in large 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.
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