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

Pejman Rasti; Rosa Huaman; Charlotte Riviere; David Rousseau

doi:10.1117/12.2303706

24 May 2018 Supervised machine learning for 3D microscopy without manual annotation: application to spheroids

Pejman Rasti, Rosa Huaman , Charlotte Riviere, David Rousseau

Proceedings Volume 10677, Unconventional Optical Imaging; 1067728 (2018) https://doi.org/10.1117/12.2303706
Event: SPIE Photonics Europe, 2018, Strasbourg, France

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.

Conference Presentation

Citation Download Citation

Pejman Rasti, Rosa Huaman , Charlotte Riviere, and David Rousseau "Supervised machine learning for 3D microscopy without manual annotation: application to spheroids", Proc. SPIE 10677, Unconventional Optical Imaging, 1067728 (24 May 2018); https://doi.org/10.1117/12.2303706

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