Joint Holographic Detection and Reconstruction

Yellin, Florence; Béjar, Benjamín; Haeffele, Benjamin D.; Mathieu, Evelien; Pick, Christian; Ray, Stuart C.; Vidal, René

doi:10.1007/978-3-030-32692-0_76

Cited by 1 publication

(1 citation statement)

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“…These advantages have led to LFI being explored as a potential solution for various applications in biomedical microscopy in resource-limited settings [4,5], such as semen [6] and hematological analysis [7][8][9][10][11][12]. Likewise, in this work we are interested in exploiting these advantages of LFI systems to develop a compact and low-cost system that is capable of measuring the concentration of platelets in human blood.…”

Section: Introductionmentioning

confidence: 99%

Generative optical modeling of whole blood for detecting platelets in lens-free images

Haeffele

Pick

Lin

et al. 2020

Biomed. Opt. Express

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In this paper, we consider the task of detecting platelets in images of diluted whole blood taken with a lens-free microscope. Despite having several advantages over traditional microscopes, lens-free imaging systems have the significant challenge that the resolution of the system is typically limited by the pixel dimensions of the image sensor. As a result of this limited resolution, detecting platelets is very difficult even by manual inspection of the images due to the fact that platelets occupy just a few pixels of the reconstructed image. To address this challenge, we develop an optical model of diluted whole blood to generate physically realistic simulated holograms suitable for training machine learning models in a supervised manner. We then use this model to train a convolutional neural network (CNN) for platelet detection and validate our approach by developing a novel optical configuration which allows collecting both lens-free and fluorescent microscopy images of the same field of view of diluted whole blood samples with fluorescently labeled platelets.

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