# Fast GPU-Enabled Color Normalization for Digital Pathology

## TL;DR

This paper introduces a GPU-accelerated, open-source color normalization method for digital pathology that improves accuracy and speed, enabling efficient processing of gigapixel whole slide images.

## Contribution

It presents algorithmic improvements and a software rewrite that make structure preserving color normalization faster, more accurate, and capable of handling large whole slide images automatically.

## Key findings

- Achieved a multifold speedup on gigapixel images.
- Reduced artifacts such as color basis swapping and background tinges.
- Software is ready-to-use for the pathology community.

## Abstract

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.