Theia: Bleed-Through Estimation with Convolutional Neural Networks

Abstract

Microscopy is ubiquitous in biological research, and with high content screening there is a need to analyze images at scale. High content screening often uses multichannel, epifluorescence microscopy (multiplexing), and fluorescent images often exhibit channel mixing, or bleed-through effects, which need to be corrected before subsequent analysis (e.g. segmentation, feature extraction, etc). In this paper we present Theia, an algorithm for bleed-through correction that requires little to no a priori information about the source or content of the images (i.e. number of channels). Theia uses a novel neural network architecture inspired by Siamese Networks and Least Absolute Shrinkage and Selection Operator (LASSO) regression to learn convolutional filters that remove bleed-through. We use metrics for quantifying bleed-through, and show Theia exhibits good capacity for removing bleed-through on both synthetic and real fluorescent images. Theia was benchmarked to demonstrate scalability across diverse datasets with varying degrees of bleed-through and numbers of channels. Since Theia learns a set of convolutional kernels using popular neural network frameworks, it can make use of GPU acceleration when scaling to large datasets.