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 overcome 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
context. DistNet2D considers seven frames at the input and uses a
post-processing procedure that exploits information from the entire movie 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 has been 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.

利用 DistNet2D 模型进行细胞分割和跟踪的研究，该模型利用长期的时间背景信息来提高分割准确性，取得了在两组实验数据上比现有方法更好的性能，最终将其应用于细胞形态与运输属性的相关性研究。