Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

ABSTRACT: 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 is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included c

SUBMITTER: Scherr T 

PROVIDER: S-EPMC7723299 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature