  3D fluorescence microscopy of living organisms has increasingly become an
essential and powerful tool in biomedical research and diagnosis. An exploding
amount of imaging data has been collected, whereas efficient and effective
computational tools to extract information from them are still lagging behind.
This is largely due to the challenges in analyzing biological data. Interesting
biological structures are not only small, but are often morphologically
irregular and highly dynamic. Although tracking cells in live organisms has
been studied for years, existing tracking methods for cells are not effective
in tracking subcellular structures, such as protein complexes, which feature in
continuous morphological changes including 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 the 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 object portions using an extended search.
Experimental results confirm that the proposed method achieves 2.96% higher on
consistent tracking accuracy and 35.48% higher on event identification accuracy
than the state-of-art methods.

Image and Video Processing;Computer Vision and Pattern Recognition