FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology

• Published in: PloS one Vol. 15; no. 10; p. e0233198
• Main Authors: Serafin, Robert, Xie, Weisi, Glaser, Adam K., Liu, Jonathan T. C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2020; Public Library of Science (PLoS)
• Subjects: Absorptivity; Algorithms; Biology and Life Sciences; Clinical pathology; Color; Color imagery; Color processing software; Coloring; Coloring Agents; Computer and Information Sciences; Data processing; Datasets; Digital imaging; Equipment and supplies; Fluorescence; Fluorescence microscopy; Freeware; Histology; Humans; Image processing; Imaging, Three-Dimensional - methods; Leveling; Mechanical engineering; Medicine and Health Sciences; Methods; Microscopy; Microscopy, Fluorescence - methods; Nondestructive testing; Open source software; Pathology; Pathology - methods; Physical Sciences; Research and Analysis Methods; Software; Source code; Staining; Staining and Labeling; Stains & staining; Technology application; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0233198

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.