Joint Multi-frame Detection and Segmentation for Multi-cell Tracking
This work addresses multi-cell tracking in biomedical imaging, which is incremental as it builds on existing tracking-by-detection methods with enhancements for dense populations.
The paper tackles the problem of tracking living cells in video sequences by improving detection and segmentation, particularly for cells in mitosis, and demonstrates state-of-the-art performance in experiments.
Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of cells. Detection performance of cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-cell tracking. A mitosis detection algorithm is proposed to detect cell mitosis and the cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, cells can be fine segmented in highly dense cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.