Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan:75:102251.
doi: 10.1016/j.media.2021.102251. Epub 2021 Nov 3.

High resolution histopathology image generation and segmentation through adversarial training

Affiliations

High resolution histopathology image generation and segmentation through adversarial training

Wenyuan Li et al. Med Image Anal. 2022 Jan.

Abstract

Semantic segmentation of histopathology images can be a vital aspect of computer-aided diagnosis, and deep learning models have been effectively applied to this task with varying levels of success. However, their impact has been limited due to the small size of fully annotated datasets. Data augmentation is one avenue to address this limitation. Generative Adversarial Networks (GANs) have shown promise in this respect, but previous work has focused mostly on classification tasks applied to MR and CT images, both of which have lower resolution and scale than histopathology images. There is limited research that applies GANs as a data augmentation approach for large-scale image semantic segmentation, which requires high-quality image-mask pairs. In this work, we propose a multi-scale conditional GAN for high-resolution, large-scale histopathology image generation and segmentation. Our model consists of a pyramid of GAN structures, each responsible for generating and segmenting images at a different scale. Using semantic masks, the generative component of our model is able to synthesize histopathology images that are visually realistic. We demonstrate that these synthesized images along with their masks can be used to boost segmentation performance, especially in the semi-supervised scenario.

Keywords: Data augmentation; Histopathology image generation; Semantic segmentation; Semi-supervised learning.

PubMed Disclaimer

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

LinkOut - more resources