ArXiv Preprint
Automated synthesis of histology images has several potential applications in
computational pathology. However, no existing method can generate realistic
tissue images with a bespoke cellular layout or user-defined histology
parameters. In this work, we propose a novel framework called SynCLay
(Synthesis from Cellular Layouts) that can construct realistic and high-quality
histology images from user-defined cellular layouts along with annotated
cellular boundaries. Tissue image generation based on bespoke cellular layouts
through the proposed framework allows users to generate different histological
patterns from arbitrary topological arrangement of different types of cells.
SynCLay generated synthetic images can be helpful in studying the role of
different types of cells present in the tumor microenvironmet. Additionally,
they can assist in balancing the distribution of cellular counts in tissue
images for designing accurate cellular composition predictors by minimizing the
effects of data imbalance. We train SynCLay in an adversarial manner and
integrate a nuclear segmentation and classification model in its training to
refine nuclear structures and generate nuclear masks in conjunction with
synthetic images. During inference, we combine the model with another
parametric model for generating colon images and associated cellular counts as
annotations given the grade of differentiation and cell densities of different
cells. We assess the generated images quantitatively and report on feedback
from trained pathologists who assigned realism scores to a set of images
generated by the framework. The average realism score across all pathologists
for synthetic images was as high as that for the real images. We also show that
augmenting limited real data with the synthetic data generated by our framework
can significantly boost prediction performance of the cellular composition
prediction task.
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
2022-12-28
