29 March 2013 Dynamically constrained pipeline for tracking neural progenitor cells
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Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 86760B (2013) https://doi.org/10.1117/12.2006996
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Large scale in vitro cell growth experiments require automated segmentation and tracking methods to construct cell lineages in order to aid cell biologists in further analysis. Flexible segmentation algorithms that easily adapt to the specific type of problem at hand and directly applicable tracking methods are fundamental building blocks of setting up multi purpose pipelines. Segmentation by discriminative dictionary learning and a graph formulated tracking method constraining the allowed topology changes are combined here to accommodate for highly irregular cell shapes and movement patterns. A mitosis detector constructed from empirical observations of cells in a pre-mitotic state interacts with the graph formulation to dynamically allow for cell mitosis when appropriate. Track consistency is ensured by introducing pragmatic constraints and the notion of blob states. We validate the proposed pipeline by tracking pig neural progenitor cells through a time lapse experiment consisting of 825 images collected over 69 hours. Each step of the tracking pipeline is validated separately by comparison with manual annotations. The number of tracked cells increase from approximately 350 to 650 during the time period.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jacob S. Vestergaard, Jacob S. Vestergaard, Anders L. Dahl, Anders L. Dahl, Peter Holm, Peter Holm, Rasmus Larsen, Rasmus Larsen, } "Dynamically constrained pipeline for tracking neural progenitor cells", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760B (29 March 2013); doi: 10.1117/12.2006996; https://doi.org/10.1117/12.2006996
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