Tracking Topological Defects in 2D Active Nematics Using Convolutional Neural Networks

The motion of topological defects in active nematics has been modeled by a number of hydrodynamic theories that remain to be fully tested, which we will do by measuring defect dynamics using video microscopy and comparing with theory. However, classical image processing techniques are cumbersome, as variations in image quality and defect morphology require fine tuning for each data set and impede high-throughput processing of experimental data. Here, we use a Convolutional Neural Network (CNN) to efficiently and precisely measure the locations of active defects. We construct a deep CNN to train a defect detector to automatically analyze videos of a microtubule-based active nematic. We labeled 8800 images from which we selected 6600 as training dataset and 1700 as testing dataset. To obtain higher precision, we also consider the temporal relation between the location of defects within consecutive frames and train our CNN correspondingly. We compare results obtained wtih CNN with results generated by traditional image processing algorithms.