Microtubule group movement tracking based on endpoints by optical flow and cluster matching

Abstract

Microtubules usually form groups at high densities and can glide in the same direction on a kinesin-coated glass surface. They tend to move together (snuggling) to avoid collisions and overlaps. Tracking microtubule groups is difficult due to their intrinsically complex and nonlinear behavior, as well as their sudden appearance and disappearance. We developed a microtubule motion analysis workflow incorporating a U-Net-like fully convolutional neural network (FCN) for noise filtering, a template testing method for endpoint detection, Sparse Optical Flow (SOF) for motion detection, SOF clustering for group detection, and cluster matching for group tracking. We fine-tuned the parameters using videos generated by a microtubule gliding simulation system, and then applied this workflow to real experimental videos. This workflow significantly facilitates microtubule motion analysis.