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Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy
2020
PLoS ONE
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method
doi:10.1371/journal.pone.0243219
pmid:33290432
fatcat:sevvza242bdkhm76oaiucyface