Super-Focus: Domain Adaptation for Embryo Imaging via Self-supervised Focal Plane Regression

Abstract

In recent years, the field of embryo imaging has seen an influx of work using machine learning. These works take advantage of large microscopy datasets collected by fertility clinics as routine practice through relatively standardised imaging setups. Nevertheless, systematic variations still exist between datasets and can harm the ability of machine learning models to perform well across different clinics. In this work, we present Super-Focus, a method for correcting systematic variations present in embryo focal stacks by artificially generating focal planes. We demonstrate that these artificially generated planes are realistic to human experts and that using Super-Focus as a pre-processing step improves the ability of a cell instance segmentation model to generalise across multiple clinics.