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Supervised machine learning for 3D microscopy without manual annotation: application to spheroids
Author(s): Pejman Rasti; Rosa Huaman ; Charlotte Riviere; David Rousseau
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Paper Abstract

We demonstrate the possibility to realize supervised machine learning for a cell detection task without having to manually annotate images through the sole use of synthetic images in the training and testing steps of the learning process. This is successfully illustrated on 3D cellular aggregates observed under light sheet fluorescence microscopy with a shallow and deep learning detection approach. A performance of more than 90% of good detection is obtained on real images.

Paper Details

Date Published: 24 May 2018
PDF: 6 pages
Proc. SPIE 10677, Unconventional Optical Imaging, 1067728 (24 May 2018); doi: 10.1117/12.2303706
Show Author Affiliations
Pejman Rasti, LARIS, IRHS UMR INRA, Univ. d'Angers (France)
Rosa Huaman , LARIS, IRHS UMR INRA, Univ. d'Angers (France)
Charlotte Riviere, Univ. Claude Bernard Lyon 1, CNRS, Institut Lumière Matière (France)
David Rousseau, LARIS, IRHS UMR INRA, Univ. d'Angers (France)


Published in SPIE Proceedings Vol. 10677:
Unconventional Optical Imaging
Corinne Fournier; Marc P. Georges; Gabriel Popescu, Editor(s)

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