AIMC Topic: Holography

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High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

Journal of biomedical optics
SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell le...

Label-free detection of cysts using a deep learning-enabled portable imaging flow cytometer.

Lab on a chip
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color...

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.

Journal of biomedical optics
SIGNIFICANCE: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase im...

Red blood cell classification in lensless single random phase encoding using convolutional neural networks.

Optics express
Rapid cell identification is achieved in a compact and field-portable system employing single random phase encoding to record opto-biological signatures of living biological cells of interest. The lensless, 3D-printed system uses a diffuser to encode...

In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in ...

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

Journal of biomedical optics
SIGNIFICANCE: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biops...

Translational and rotational manipulation of filamentous cells using optically driven microrobots.

Optics express
Optical cell manipulation has become increasingly valuable in cell-based assays. In this paper, we demonstrate the translational and rotational manipulation of filamentous cells using multiple cooperative microrobots automatically driven by holograph...

Label-free optical hemogram of granulocytes enhanced by artificial neural networks.

Optics express
An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to id...

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks.

Optics express
Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imag...