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

Scherr, Tim; Löffler, Katharina; Böhland, Moritz; Mikut, Ralf

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 cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000127750
Veröffentlicht am 15.12.2020
DOI: 10.1371/journal.pone.0243219
Zitationen: 3
Web of Science
Zitationen: 2
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1932-6203
KITopen-ID: 1000127750
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in PLOS ONE
Verlag Public Library of Science (PLoS)
Band 15
Heft 12
Seiten Art. Nr.: e0243219
Bemerkung zur Veröffentlichung Gefördert vom Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (MWK) im Rahmen des Open-Access-Förderprogramms "BW BigDIWA"
Vorab online veröffentlicht am 08.12.2020
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