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

TL;DR

This paper introduces a novel deep feature map-based method for tracking subcellular structures in 4D fluorescence microscopy, effectively handling morphological changes and complex motions to improve tracking accuracy.

Contribution

It presents a new multi-object portion tracking approach utilizing deep feature maps and extended search, specifically designed for irregular, dynamic biological structures.

Findings

Achieves 2.96% higher consistent tracking accuracy

Improves event identification accuracy by 35.48%

Effectively handles morphological changes and complex motions

Abstract

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.