Deep Anomaly Generation: An Image Translation Approach of Synthesizing Abnormal Banded Chromosome Images

TL;DR

This paper introduces a novel image translation method using conditional GANs to generate realistic synthetic chromosome images with specific banding patterns, aiding data augmentation in cytogenetics.

Contribution

It presents a new approach combining image-to-image translation and self-generated segmentation maps for realistic chromosome image synthesis.

Findings

Successfully generated chromosomes with seen banding patterns.

Effective synthesis of unseen banding patterns.

Potential for improving data augmentation in cytogenetics.

Abstract

Advances in deep-learning-based pipelines have led to breakthroughs in a variety of microscopy image diagnostics. However, a sufficiently big training data set is usually difficult to obtain due to high annotation costs. In the case of banded chromosome images, the creation of big enough libraries is difficult for multiple pathologies due to the rarity of certain genetic disorders. Generative Adversarial Networks (GANs) have proven to be effective in generating synthetic images and extending training data sets. In our work, we implement a conditional adversarial network that allows generation of realistic single chromosome images following user-defined banding patterns. To this end, an image-to-image translation approach based on self-generated 2D chromosome segmentation label maps is used. Our validation shows promising results when synthesizing chromosomes with seen as well as unseen banding patterns. We believe that this approach can be exploited for data augmentation of chromosome data sets with structural abnormalities. Therefore, the proposed method could help to tackle medical image analysis problems such as data simulation, segmentation, detection, or classification in the field of cytogenetics.