DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking

TL;DR

DistNet2D is a novel deep neural network architecture that effectively leverages both short- and long-term temporal information to improve 2D cell segmentation and tracking accuracy in complex videomicroscopy datasets.

Contribution

We introduce DistNet2D, a new DNN architecture that incorporates multi-scale temporal context and a post-processing step for enhanced segmentation and tracking performance.

Findings

DistNet2D outperforms recent methods on dense bacterial and eukaryotic cell datasets.

The method enables accurate correlation of cell morphology with transport properties.

Integration into ImageJ facilitates practical application and data analysis.

Abstract

Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.