Consistent Cell Tracking in Multi-Frames With Spatio-Temporal Context by Object-Level Warping Loss

Junya Hayashida, Kazuya Nishimura, Ryoma Bise; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1727-1736

Multi-object tracking is essential in biomedical image analysis. Most multi-object tracking methods follow a tracking-by-detection approach that involves using object detectors and learning the appearance feature models of the detected regions for association. Although these methods can learn the appearance similarity features to identify the same objects among frames, they have difficulties identifying the same cells because cells have a similar appearance and their shapes change as they migrate. In addition, cells often partially overlap for several frames. In this case, even an expert biologist would require knowledge of the spatial-temporal context in order to identify individual cells. To tackle such difficult situations, we propose a cell-tracking method that can effectively use the spatial-temporal context in multiple frames by using long-term motion estimation and an object-level warping loss. We conducted experiments showing that the proposed method outperformed state-of-the-art methods under various conditions on real biological images.