The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Pix2Pix-based Stain-to-Stain Translation:A Solution for Robust StainNormalization in Histopathology ImagesAnalysis

Pegah Salehi, Abdolah Chalechale
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the “pix2pix” framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available.

Keywords: Histopathology Images, Stain Normalization, Stain-to-Stain Translation (STST), Conditional Generative Adversarial Network (cGAN), Deep Learning



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