In this paper, we address the problem of estimating the migration direction of cells based on a single image. A solution to this problem lays the foundation for a variety of applications that were previously not possible. To our knowledge, there is only one related work that employs a classification CNN with four classes (quadrants). However, this approach does not allow for detailed directional resolution. We tackle the single image estimation problem using deep circular regression, with a particular focus on cycle-sensitive methods. On two common datasets, we achieve a mean estimation error of ∼17∘, representing a significant improvement over previous work, which reported estimation error of 30∘ and 34∘, respectively.
@article{arxiv.2406.19162,
title = {Single Image Estimation of Cell Migration Direction by Deep Circular Regression},
author = {Lennart Bruns and Lucas Lamparter and Milos Galic and Xiaoyi Jiang},
journal= {arXiv preprint arXiv:2406.19162},
year = {2025}
}