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Supervised machine learning for 3D microscopy without manual annotation: Application to spheroids

Pejman Rasti 1 R. Huaman Charlotte Riviere 2 David Rousseau 1
1 LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes
2 ILM - Institut Lumière Matière [Villeurbanne]
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.
Keywords : S upervised machine learning 3D microscopy cell detection
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Conference papers
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https://hal-univ-lyon1.archives-ouvertes.fr/hal-02289873
Contributor : Marie-Gabrielle Chautard <>
Submitted on : Tuesday, September 17, 2019 - 10:59:19 AM
Last modification on : Thursday, October 15, 2020 - 8:54:04 AM

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UNIV-LYON1 | ILM | CNRS | UDL | UNIV-ANGERS | LARIS

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Pejman Rasti, R. Huaman, Charlotte Riviere, David Rousseau. Supervised machine learning for 3D microscopy without manual annotation: Application to spheroids. SPIE PHOTONICS EUROPE, Society of Photographic Instrumentation Engineers (SPIE). GBR., Apr 2018, Strasbourg, France. pp.1067728, ⟨10.1117/12.2303706⟩. ⟨hal-02289873⟩

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