Deep Learning For Particle Detection And Tracking In Fluorescence Microscopy Images

Tracking of subcellular structures displayed as spots in fluorescence microscopy images is important to quantify viral and cellular processes. We have developed a novel tracking approach for biological particles which uses deep learning for both particle detection and particle association. Our approach combines a domain adapted Deconvolution Network for particle detection with an LSTM-based recurrent neural network for tracking. Past and future information in both forward and backward direction is exploited by bidirectional LSTMs, and assignment probabilities are determined jointly across multiple detections. We evaluated the proposed approach using image sequences of the Particle Tracking Challenge as well as live cell fluorescence microscopy data of hepatitis C virus proteins. It turned out that our approach yields state-of-the-art results or improves the results compared to previous methods.