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

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

Citation Download Citation

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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