Automatic detection of subviral particles in fluorescence microscopy images of Marburg virus infected cells using a convolutional neural network

Abstract

Abstract In collaboration with the Institute of Virology at the University of Marburg, a tracking algorithm has been developed in recent years. Thereby, for the tracking of subviral particles and their intracellular transport through the cell, the detection of these particles is the first major step. In this work a deep-learning based approach for automatic processing of fluorescence video sequences of virus infected cells is presented. The focus of this work was to generate synthetic data for training neuronal networks and to represent a particle using only a single delta peak. It was shown that a simple convolutional network is able to predict the particle positions with an almost identical accuracy as the algorithm developed by Kienzle, Rausch and colleagues [1-3], and that even a small dataset is sufficient for that purpose.