Single Image Estimation of Cell Migration Direction by Deep Circular Regression

TL;DR

This paper introduces a deep circular regression method to accurately estimate cell migration directions from a single image, significantly improving resolution and accuracy over previous classification-based approaches.

Contribution

The paper presents a novel deep circular regression approach for single image cell migration direction estimation, enabling finer directional resolution than prior classification methods.

Findings

Achieved mean estimation error of ~17° on two datasets

Significantly outperformed previous methods with errors of 30° and 34°

Demonstrated effectiveness of cycle-sensitive deep regression techniques

Abstract

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 $\sim\!17^\circ$, representing a significant improvement over previous work, which reported estimation error of $30^\circ$ and $34^\circ$, respectively.