Cell tracking-by-detection using elliptical bounding boxes

Abstract

Cell detection and tracking are crucial for bio-analysis. Current approaches rely on the tracking-by-model evolution paradigm, where end-to-end deep learning models are trained for cell detection and tracking. However, such methods require extensive amounts of annotated data, which is time-consuming and often requires specialized annotators. The proposed method involves approximating cell shapes as oriented ellipses and utilizing generic-purpose-oriented object detectors for cell detection to alleviate the requirement of annotated data. A global data association algorithm is then employed to explore temporal cell similarity using probability distance metrics, considering that the ellipses relate to two-dimensional Gaussian distributions. The results of this study suggest that the proposed tracking-by-detection paradigm is a viable alternative for cell tracking. The method achieves competitive results and reduces the dependency on extensive annotated data, addressing a common challenge in current cell detection and tracking approaches. Our code is publicly available at https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.