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Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

View ORCID ProfileDiego Ulisse Pizzagalli, Marcus Thelen, Santiago Fernandez Gonzalez, Rolf Krause
doi: https://doi.org/10.1101/829838
Diego Ulisse Pizzagalli
1Faculty of Biomedical Science, USI, Institute for Research in Biomedicine, CH6500 Bellinzona – Switzerland
2Institute of Computational Science, USI, CH6900 Lugano – Switzerland
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  • ORCID record for Diego Ulisse Pizzagalli
Marcus Thelen
1Faculty of Biomedical Science, USI, Institute for Research in Biomedicine, CH6500 Bellinzona – Switzerland
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Santiago Fernandez Gonzalez
1Faculty of Biomedical Science, USI, Institute for Research in Biomedicine, CH6500 Bellinzona – Switzerland
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  • For correspondence: santiago.gonzalez@irb.usi.ch rolf.krause@usi.ch
Rolf Krause
2Institute of Computational Science, USI, CH6900 Lugano – Switzerland
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  • For correspondence: santiago.gonzalez@irb.usi.ch rolf.krause@usi.ch
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Abstract

2-photon intravital microscopy (2P-IVM) is a key technique to investigate cell migration and cell-to-cell interactions in organs and tissues of living organisms. Focusing on immunology, 2P-IVM allowed recording videos of leukocytes during the immune response, highlighting unprecedented mechanisms of the immune system. However, the automatic analysis of the acquired videos remains challenging and poorly reproducible. In fact, both manual curation of results and tuning of bioimaging software parameters among different experiments, are required. One of the most difficult tasks for a user is transferring to a computer the knowledge on what a cell is and how it should appear with respect to the background, other objects, or other cell types. This is possibly due to the low specificity of acquisition channels which may include multiple cell populations and the presence of similar objects in the background.

In this work, we propose a method based on semi-supervised machine learning to facilitate colocalization. In line with recently proposed approaches for pixel classification, the method requires the user to draw some lines on the cells of interest and some line on the other objects/background. These lines embed knowledge, not only on which pixel belongs to a class or which pixel belongs to another class but also on how pixels in the same object are connected. Hence, the proposed method exploits the information from the lines to create an additional imaging channel that is specific for the cells fo interest. The usage of this method increased tracking accuracy on a dataset of challenging 2P-IVM videos of leukocytes. Additionally, it allowed processing multiple samples of the same experiment keeping the same mathematical model.

Footnotes

  • Added missing figure with tracking improvements Corrected wrong (duplicated) figure legend

  • http://www.ltdb.info/tools/

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 07, 2019.
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Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy
Diego Ulisse Pizzagalli, Marcus Thelen, Santiago Fernandez Gonzalez, Rolf Krause
bioRxiv 829838; doi: https://doi.org/10.1101/829838
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Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy
Diego Ulisse Pizzagalli, Marcus Thelen, Santiago Fernandez Gonzalez, Rolf Krause
bioRxiv 829838; doi: https://doi.org/10.1101/829838

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