Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis

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 1 .

Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks

“…To generate the NIR band from RGB, we used cGAN. We chose the Pix2pix approach for this task because it performs quite well for image translation problems [ 40 , 41 ]. For the generator, we used the U-Net [ 42 ] architecture with the Resnet-34 [ 43 ] encoder.…”

Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

“…The listed color normalization approaches are based on a style transfer method in which the style of the input image is modified based on the style image, when preserving the content of the input image. [ 37 39 50 51 53 54 55 ] The methods based on cycleGAN explore the capability of unpaired image-to-image translation which makes it a flexible architecture for stain normalization. Other approaches discussed here use alternative formulations such as self-attention models,[ 56 ] cGAN,[ 31 ] and encoder–decoder architecture.…”

StainNet: A Fast and Robust Stain Normalization Network

“…So, stain normalization is a routine pre-processing operation for pathological images, especially for CAD systems, and it is reported to help increase the prediction accuracy, such as tumor classification (5). Stain normalization algorithms usually transfer the color style of the source image to that of a target image (6) while preserving the other information in the processed image (7), which can be broadly classified into two classes: conventional methods and deep learning-based methods.…”

“…Deep learning-based methods mostly apply generative adversarial networks (GANs) to achieve stain normalization (3,7,8,(16)(17)(18). Shaban et al (8) proposed an unsupervised stain normalization method named StainGAN based on CycleGAN (16) to transfer the stain style.…”

“…Cai et al (3) proposed a new generator to improve the image quality and accelerate the networks. On the other hand, Cho et al (18), Salehi et al (7), and Tellez et al (17) reconstructed original images from the images with color augmentations, e.g., grayscale and Hue-Saturation-Value (HSV) transformation, and tried to normalize other color styles to the original. However, due to the complexity of deep neural networks and the instability of GANs, it is hard to preserve all source information; sometimes, it has a risk of introducing some artifacts, which has some adverse effects on subsequent analysis (19).…”