  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.
