Deep Adversarial Image Synthesis for Nuclei Segmentation of Histopathology Image

Abstract

Nuclei segmentation is a fundamental upstream task of digital pathology image analysis. Existing nuclei segmentation methods usually require pixel-level labeled images from experienced pathologists. In this paper, we proposed an innovative data augmentation workflow for histopathology images: a) generates a set of initial central points randomly with existing human-annotated histopathology image datasets; b) generates nuclei segmentation masks based on the generated centroid points of step a); c) generates Haematoxylin and Eosin (H&E)-stained histopathology images corresponding to the generated nuclei masks. In addition, we proposed a deep attention feature fusion generative adversarial network (DAFF -GAN) to improve the image quality and the photorealism of the generated image. We conducted extensive experiments on several existing nuclei segmentation methods, comparing using raw data with the augmented data by our strategy. Extensive experiments proved the effectiveness of our proposed strategy.