  Segmentation of Prostate Cancer (PCa) tissues from Gleason graded
histopathology images is vital for accurate diagnosis. Although deep learning
(DL) based segmentation methods achieve state-of-the-art accuracy, they rely on
large datasets with manual annotations. We propose a method to synthesize for
PCa histopathology images by learning the geometrical relationship between
different disease labels using self-supervised learning. We use a weakly
supervised segmentation approach that uses Gleason score to segment the
diseased regions and the resulting segmentation map is used to train a Shape
Restoration Network (ShaRe-Net) to predict missing mask segments in a
self-supervised manner. Using DenseUNet as the backbone generator architecture
we incorporate latent variable sampling to inject diversity in the image
generation process and thus improve robustness. Experiments on multiple
histopathology datasets demonstrate the superiority of our method over
competing image synthesis methods for segmentation tasks. Ablation studies show
the benefits of integrating geometry and diversity in generating high-quality
images, and our self-supervised approach with limited class-labeled data
achieves similar performance as fully supervised learning.
