Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery With Deep Feature Maps

Yang Jiao, Mo Weng, Mei Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. Exploding amount of imaging data has been collected whereas efficient and effective computational tools to extract information from them are still lagged behind. This largely is due to the challenges in analyzing biological data. Interesting biological structures are not only small but often are morphologically irregular and highly dynamic. Tracking cells in live organisms has been studied for years as a sophisticated mission in bioinformatics. However, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes, such as split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches objects' portions using extended search. Experimental results confirm that the proposed method achieves 2.98% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy.