Intelligent Image De‐Blurring for Imaging Flow Cytometry

Zhang, Fangzheng; Lei, Cheng; Huang, Chun-Jung; Kobayashi, Hirofumi; Sun, Chia‐Wei; Goda, Keisuke

doi:10.1002/cyto.a.23771

Cited by 15 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their work provided inspiring insights into the deep-learning-based enhancement of biological images, but cannot be directly applied to phase-contrast cell images. Zhang et al [ 41 ] achieved a similar target of the deblurring of defocused cell images like us. However, they concentrated on small-size grayscale images of single cells collected in flow cytometry.…”

Section: Discussionmentioning

confidence: 95%

A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments

Xue

Qian

et al. 2022

Bioactive Materials

View full text Add to dashboard Cite

The increasing throughput of experiments in biomaterials research makes automatic techniques more and more necessary. Among all the characterization methods, microscopy makes fundamental contributions to biomaterials science where precisely focused images are the basis of related research. Although automatic focusing has been widely applied in all kinds of microscopes, defocused images can still be acquired now and then due to factors including background noises of materials and mechanical errors. Herein, we present a deep-learning-based method for the automatic sorting and reconstruction of defocused cell images. First, the defocusing problem is illustrated on a high-throughput cell microarray. Then, a comprehensive dataset of phase-contrast images captured from varied conditions containing multiple cell types, magnifications, and substrate materials is prepared to establish and test our method. We obtain high accuracy of over 0.993 on the dataset using a simple network architecture that requires less than half of the training time compared with the classical ResNetV2 architecture. Moreover, the subcellular-level reconstruction of heavily defocused cell images is achieved with another architecture. The applicability of the established workflow in practice is finally demonstrated on the high-throughput cell microarray. The intelligent workflow does not require a priori knowledge of focusing algorithms, possessing widespread application value in cell experiments concerning high-throughput or time-lapse imaging.

show abstract

Section: Discussionmentioning

confidence: 95%

A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments

Xue

Qian

et al. 2022

Bioactive Materials

View full text Add to dashboard Cite

show abstract

“…However, in imaging flow cytometry, a large dataset is relatively easy to collect. Therefore, it is convenient to use neural networks in flow cytometry with visualization; for example, the residual dense network can be used to eradicate blurring [99].…”

Section: Application Of Deep Learning In a Deconvolution Problemmentioning

confidence: 99%

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

Makarkin

Bratashov

2021

Micromachines

View full text Add to dashboard Cite

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.

show abstract

“…The principle of this part is to use the focusing curve to manage the work of the stepper motor, control the movement of the objective lens in all directions, and help the objective lens to focus, including the design of the microscope control board and the extreme value search of the image processing part [13] . Among them, the role of the control board is to use the microscope to select the scene to be observed and optimize the image to be clear: Take the motion controller TMS32UF2812 for TI as its main component.…”

Section: Feedback Control Modulementioning

confidence: 99%

Intelligent Fuzzy Focusing System For Dark Channel Image Based On Ratio Constraint

2021

Preprint

View full text Add to dashboard Cite

The starting point of the establishment of the optimized dark channel algorithm is to stabilize the contrast of the two parts of the image (light and dark). The first step of this method is to divide the initial image into two suitable light and dark parts, and then to obtain the contrast data between the two parts, and the second step is to solve the dark area according to the above optimized operation principle. The third step is to exert its accurate management bright features to the extreme to ensure its contrast stability through the operation of double histogram equalization. Modify the unreasonable arrangement of brightness. Take the CCD or CMOS image sensor as an example, the reference will be shown in the above example according to the lens system, and then the intelligent chip of the non-manually operated focusing device will complete the next processing, transmitting the discrimination result to the front system through the motor, and finally focusing. The intelligent fuzzy focusing is composed of the upper and lower computers, the former is the module of collating, collecting materials and processing all the data, the latter is the management and control module of the evaluation results, and the communication part provides communication for the two. Finally, it can be seen that the optimized dark channel algorithm is obviously better than the de-fog algorithm in terms of the effect of information entropy, brightness and average gradient, which makes the detailed characteristics of being obscured by fog more obvious.

show abstract

Intelligent Image De‐Blurring for Imaging Flow Cytometry

Cited by 15 publications

References 26 publications

A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments

A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments

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

Intelligent Fuzzy Focusing System For Dark Channel Image Based On Ratio Constraint

Contact Info

Product

Resources

About