Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps
This work addresses a domain-specific problem in biomedical imaging for researchers needing to analyze dynamic subcellular structures, representing an incremental improvement over existing tracking methods.
The paper tackles the problem of tracking subcellular structures like protein complexes in 4D fluorescence microscopy, which are challenging due to morphological changes and motion, and reports a method that achieves 2.96% higher consistent tracking accuracy and 35.48% higher event identification accuracy than state-of-the-art methods.
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.