FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images

Abstract

Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this process, however, high-performance algorithms have not been developed. The limitations in image acquisition, especially for intracellular parasites, make this process complex. For this reason, traditional image-processing methods are not easily transferred between different datasets and segmentation-based strategies do not have a high performance. Here, we propose a novel method FiCRoN, based on fully convolutional regression networks (FCRNs), as a promising new tool for estimating intracellular parasite burden. This estimation requires three values, intracellular parasites, infected cells and uninfected cells. FiCRoN solves this problem as multi-task learning: counting by regression at two scales, a smaller one for intracellular parasites and a larger one for host cells. It does not use segmentation or detection, resulting in a higher generalization of counting tasks and, therefore, a decrease in error propagation. Linear regression reveals an excellent correlation coefficient between manual and automatic methods. FiCRoN is an innovative freedom-respecting image analysis software based on deep learning, designed to provide a fast and accurate quantification of parasite burden, also potentially useful as a single-cell counter.