Coded illumination for motion-blur free imaging of cells on cell-phone based imaging flow cytometer

Saxena, Manish; Gorthi, Sai Siva

doi:10.1063/1.4898252

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…These include scattering (random disturbance of light caused by differences in the sample's refractive index and its environment), glare (random disturbances caused by the unexpected appearance of a beam of light with inappropriate polarization), and blur. Blur often appears when recording an image of a moving sample or if the camera does not have enough temporal resolution-this is referred to as motion blur [3]. When this sample is displaced from focus, this is referred to as defocusing blur [4].…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

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Section: Introductionmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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“…This creates a bottleneck in the form of low-flow velocities and affects the throughput of cells being analyzed. 6 Lower flow velocities allow more time for cells to settle down out of suspension in their sample reservoirs or syringe pumps. This settling in turn results in nonuniform microfluidic loading of cells over time, leading to incorrect cell counts.…”

Section: Introductionmentioning

confidence: 99%

“…This settling in turn results in nonuniform microfluidic loading of cells over time, leading to incorrect cell counts. To overcome this problem of cell settling, techniques such as physical agitation of cells, 6 magnetic stirring, 7,8 and chemical methods 9 have been used in previous studies. While effective, these techniques were developed for use in laboratory settings and are difficult to implement in point-of-care settings, especially in resource-poor environments.…”

Section: Introductionmentioning

confidence: 99%

Automated Blood Sample Preparation Unit (ABSPU) for Portable Microfluidic Flow Cytometry

Chaturvedi

Gorthi

2017

SLAS Technology

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Portable microfluidic diagnostic devices, including flow cytometers, are being developed for point-of-care settings, especially in conjunction with inexpensive imaging devices such as mobile phone cameras. However, two pervasive drawbacks of these have been the lack of automated sample preparation processes and cells settling out of sample suspensions, leading to inaccurate results. We report an automated blood sample preparation unit (ABSPU) to prevent blood samples from settling in a reservoir during loading of samples in flow cytometers. This apparatus automates the preanalytical steps of dilution and staining of blood cells prior to microfluidic loading. It employs an assembly with a miniature vibration motor to drive turbulence in a sample reservoir. To validate performance of this system, we present experimental evidence demonstrating prevention of blood cell settling, cell integrity, and staining of cells prior to flow cytometric analysis. This setup is further integrated with a microfluidic imaging flow cytometer to investigate cell count variability. With no need for prior sample preparation, a drop of whole blood can be directly introduced to the setup without premixing with buffers manually. Our results show that integration of this assembly with microfluidic analysis provides a competent automation tool for low-cost point-of-care blood-based diagnostics.

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Coded illumination for motion-blur free imaging of cells on cell-phone based imaging flow cytometer

Cited by 2 publications

References 12 publications

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Automated Blood Sample Preparation Unit (ABSPU) for Portable Microfluidic Flow Cytometry

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