SCASA: From Synthetic to Real Computer-Aided Sperm Analysis

Abstract

Sperm analysis has a central role in diagnosing and treating infertility. Traditionally, assessment of sperm health was performed by an expert by viewing the sample through a microscope. In order to simplify this task and assist the expert, CASA (Computer-Assisted Sperm Analysis) systems were developed. These systems rely on low-level computer vision tasks such as classification, detection and tracking to analyze sperm health and motility. These tasks have been widely addressed in the literature, with some supervised approaches surpassing the human capacity to solve them. However, the accuracy of these models have not been directly translated into CASA systems. This is mainly due to the absence of labelled data, as well as the difficulty in obtaining it. In this work we propose the generation of synthetic semen samples to tackle the absence of labelled data. We propose a parametric modelling of spermatozoa, and show how models trained on synthetic data can be used on real images with no need of further fine-tuning stage.