InTracker: An Integrated Detector-Tracker Framework for Cell Detection and Tracking

Abstract

Automatic tracking of moving cells in time-lapse image sequences plays an important role in studying many biological processes in development and diseases. Large variations in cell appearances, limited image resolution, and various cell behaviors (e.g., division, apoptosis, deformation, clustering, and migration in or out of the imaging window) make cell tracking a challenging task. However, known cell tracking methods were designed for and tailored to specific cell image sequences and behaviors, thus having limited applicability to various cell image sequences. Aiming toward more robust cell tracking, we propose a new detector-tracker approach for detection and association based cell tracking. First, we propose a new deep learning based detector to detect cells in each image frame and assign division/non-division labels to them. Second, we carefully design an Earth Mover's Distance (EMD) based hierarchical tracker to associate detected cells through the image sequence and form moving cell trajectories. The tracker is able to correct possible detection errors made by the detector. Evaluated on several open challenge datasets, our approach outperforms state-of-the-art cell tracking methods for determining cell trajectories.