Proceedings of the 20th Python in Science Conference

  1. Varun Kapoor 1
  2. Claudia Carabaña 1
  1. 1 Institut Curie, PSL Research University, Sorbonne University, CNRSUMR3215, INSERM U934, Genetics and Developemental Biology, Paris, France
Actas:
Proceedings of the Python in Science Conference

ISSN: 2575-9752

Año de publicación: 2021

Páginas: 154-161

Tipo: Aportación congreso

DOI: 10.25080/MAJORA-1B6FD038-02B GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Live-cell imaging is a highly used technique to study cell migration and dynamics over time. Although many computational tools have beendeveloped during the past years to automatically detect and track cells, theyare optimized to detect cell nuclei with similar shapes and/or cells not clustering together. These existing tools are challenged when tracking fluorescentlylabelled membranes of cells due to cell’s irregular shape, variability in sizeand dynamic movement across Z planes making it difficult to detect and trackthem. Here we introduce a detailed analysis pipeline to perform segmentationwith accurate shape information, combined with BTrackmate, a customizedcodebase of popular ImageJ/Fiji software Trackmate, to perform cell trackinginside the tissue of interest. We developed VollSeg, a new segmentation methodable to detect membrane-labelled cells with low signal-to-noise ratio and densepacking. Finally, we also created an interface in Napari, an Euler angle basedviewer, to visualize the tracks along a chosen view making it possible to followa cell along the plane of motion. Importantly, we provide a detailed protocolto implement this pipeline in a new dataset, together with the required Jupyternotebooks. Our codes are open source available at [Git].

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