Spherical object segmentation in digital holographic microscopy by deep-learning

Carlos Valadares; Dylan Brault; Loïc Denis; Corinne Fournier

doi:10.1117/12.2559206

30 March 2020 Spherical object segmentation in digital holographic microscopy by deep-learning

Carlos Valadares, Dylan Brault, Loïc Denis, Corinne Fournier

Author Affiliations +

Proceedings Volume 11351, Unconventional Optical Imaging II; 1135120 (2020) https://doi.org/10.1117/12.2559206
Event: SPIE Photonics Europe, 2020, Online Only

Abstract

Digital holographic microscopy can image both absorbing and translucent objects. Due to the presence of twin-images and out-of-focus objects, the task of segmenting the objects from a back-propagated hologram is challenging. This paper investigates the use of deep neural networks to combine the real and imaginary parts of the back-propagated wave and produce a segmentation. The network, trained with pairs of back-propagated simulated holograms and ground truth segmentations, is shown to perform well even in the case of a mismatch between the defocus distance of the holograms used during the training step and the actual defocus distance of the holograms at test time.

Conference Presentation

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Carlos Valadares, Dylan Brault, Loïc Denis, and Corinne Fournier "Spherical object segmentation in digital holographic microscopy by deep-learning", Proc. SPIE 11351, Unconventional Optical Imaging II, 1135120 (30 March 2020); https://doi.org/10.1117/12.2559206

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