Digital holographic microscopy driven by deep learning: a study on marine planktons

Harshith Bachimanchi; Benjamin Midtvedt; Daniel Midtvedt; Erik Selander; Giovanni Volpe

doi:10.1117/12.2568508

20 August 2020 Digital holographic microscopy driven by deep learning: a study on marine planktons

Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, Giovanni Volpe

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691O (2020) https://doi.org/10.1117/12.2568508
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Conference Poster

Abstract

Digital Holographic Microscopy (DHM) has been a successful imaging technique for various applications in biomedical imaging, particle analysis, and optical engineering. Though DHM has been successful in reconstructing 3D volumes with stationary objects, it has still been a challenging task to track fast mobile objects. Recent advancements in deep learning with convolutional neural networks have been proven useful in solving experimental difficulties, starting from tracking single particles to multiple bacterial cells. Here, we propose a compact DHM driven by neural networks with a minimal amount of optical elements with an ultimate aim for easy usage and transportation.

Conference Presentation

Citation Download Citation

Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe "Digital holographic microscopy driven by deep learning: a study on marine planktons", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691O (20 August 2020); https://doi.org/10.1117/12.2568508

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