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.

翻译：本文针对基于单张图像估计细胞迁移方向的问题展开研究。该问题的解决为多种先前无法实现的应用奠定了基础。据我们所知，目前仅有一项相关工作采用四类别（象限）分类CNN方法，但该方法无法实现精细的方向分辨率。我们通过深度圆形回归技术处理单图像估计问题，特别关注周期敏感方法。在两个常用数据集上，我们实现了约17°的平均估计误差，相较于先前报道的30°和34°估计误差，取得了显著改进。