Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms

TL;DR

This paper introduces DenseTrack, a novel deep learning and mathematical modeling-based algorithm for accurate cell tracking in dense 3D microscopy videos, effectively handling cell division and crowded environments.

Contribution

The paper presents a new cell tracking algorithm combining deep sequence classification with geometric-based division detection, improving accuracy in dense bacterial biofilm videos.

Findings

Outperforms recent state-of-the-art methods in qualitative evaluation

Achieves higher accuracy in dense 3D cell tracking

Effectively detects cell division events in crowded scenarios

Abstract

Automatic cell tracking in dense environments is plagued by inaccurate correspondences and misidentification of parent-offspring relationships. In this paper, we introduce a novel cell tracking algorithm named DenseTrack, which integrates deep learning with mathematical model-based strategies to effectively establish correspondences between consecutive frames and detect cell division events in crowded scenarios. We formulate the cell tracking problem as a deep learning-based temporal sequence classification task followed by solving a constrained one-to-one matching optimization problem exploiting the classifier's confidence scores. Additionally, we present an eigendecomposition-based cell division detection strategy that leverages knowledge of cellular geometry. The performance of the proposed approach has been evaluated by tracking densely packed cells in 3D time-lapse image sequences of bacterial biofilm development. The experimental results on simulated as well as experimental fluorescence image sequences suggest that the proposed tracking method achieves superior performance in terms of both qualitative and quantitative evaluation measures compared to recent state-of-the-art cell tracking approaches.