Deep Learning Particle Detection for Probabilistic Tracking in Fluorescence Microscopy Images

Abstract

Automatic tracking of subcellular structures displayed as small spots in fluorescence microscopy images is important to quantify biological processes. We have developed a novel approach for tracking multiple fluorescent particles based on deep learning and Bayesian sequential estimation. Our approach combines a convolutional neural network for particle detection with probabilistic data association. We identified data association parameters that depend on the detection result, and automatically determine these parameters by hyperparameter optimization. We evaluated our approach based on image sequences of the Particle Tracking Challenge as well as live cell fluorescence microscopy data of hepatitis C virus proteins. It turned out that the new approach generally outperforms existing methods.