  Normalizing unwanted color variations due to differences in staining
processes and scanner responses has been shown to aid machine learning in
computational pathology. Of the several popular techniques for color
normalization, structure preserving color normalization (SPCN) is
well-motivated, convincingly tested, and published with its code base. However,
SPCN makes occasional errors in color basis estimation leading to artifacts
such as swapping the color basis vectors between stains or giving a colored
tinge to the background with no tissue. We made several algorithmic
improvements to remove these artifacts. Additionally, the original SPCN code is
not readily usable on gigapixel whole slide images (WSIs) due to long run
times, use of proprietary software platform and libraries, and its inability to
automatically handle WSIs. We completely rewrote the software such that it can
automatically handle images of any size in popular WSI formats. Our software
utilizes GPU-acceleration and open-source libraries that are becoming
ubiquitous with the advent of deep learning. We also made several other small
improvements and achieved a multifold overall speedup on gigapixel images. Our
algorithm and software is usable right out-of-the-box by the computational
pathology community.
