No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Jaferzadeh, Keyvan; Hwang, Seunghyeon; Moon, Inkyu; Javidi, Bahram

doi:10.1364/boe.10.004276

Cited by 31 publications

(16 citation statements)

References 45 publications

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“…Convolutional neural network (CNN) and deep learning approaches have been proposed for several optical applications. Examples include virtual staining of non-stained samples [33], increasing spatial resolution in a large field of view in optical microscopy [34,35], color holographic microscopy with CNN [36], autofocusing and enhancing the depth-of-filed in inline holography [37], lens-less computational imaging by deep learning [38], single-cell-based reconstruction distance estimation by a regression CNN model [39], super-resolution fringe patterns by deep learning holography [40], virtual refocusing in fluorescence microscopy to map 2D images to a 3D surface [41], and several other studies [42][43][44]. Deep-learning based phase recovery by residual CNN model was also suggested [45], but the application is limited because the reference noise-free phase images for deep-learning model are generated by the multi-height phase retrieval approach (8 holograms are recorded at different sample-to-sensor distances).…”

Section: Proposed Deep Learning Model For Phase Recoverymentioning

confidence: 99%

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network

Moon¹,

Jaferzadeh²,

Kim³

et al. 2020

Opt. Express

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This paper shows that deep learning can eliminate the superimposed twin-image noise in phase images of Gabor holographic setup. This is achieved by the conditional generative adversarial model (C-GAN), trained by input-output pairs of noisy phase images obtained from synthetic Gabor holography and the corresponding quantitative noise-free contrast-phase image obtained by the off-axis digital holography. To train the model, Gabor holograms are generated from digital off-axis holograms with spatial shifting of the real image and twin image in the frequency domain and then adding them with the DC term in the spatial domain. Finally, the digital propagation of the Gabor hologram with Fresnel approximation generates a superimposed phase image for the C-GAN model input. Two models were trained: a human red blood cell model and an elliptical cancer cell model. Following the training, several quantitative analyses were conducted on the biochemical properties and similarity between actual noise-free phase images and the model output. Surprisingly, it is discovered that our model can recover other elliptical cell lines that were not observed during the training. Additionally, some misalignments can also be compensated with the trained model. Particularly, if the reconstruction distance is somewhat incorrect, this model can still retrieve in-focus images.

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Section: Proposed Deep Learning Model For Phase Recoverymentioning

confidence: 99%

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network

Moon¹,

Jaferzadeh²,

Kim³

et al. 2020

Opt. Express

Self Cite

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“…In contrast to these algorithms, deep learning correlates an intensity distribution to a hologram without reconstruction of phase and amplitude information from an intensity distribution thanks to its data-driven approach. Deep learning is a superior tool that presents important achievement especially in holography for imaging [25][26][27][28], microscopy [29][30][31][32], optical trapping [33], and molecular diagnostics [34].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive deep learning model for 3D color holography

Yolalmaz

Yüce

2022

Sci Rep

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Holography is a vital tool used in various applications from microscopy, solar energy, imaging, display to information encryption. Generation of a holographic image and reconstruction of object/hologram information from a holographic image using the current algorithms are time-consuming processes. Versatile, fast in the meantime, accurate methodologies are required to compute holograms performing color imaging at multiple observation planes and reconstruct object/sample information from a holographic image for widely accommodating optical holograms. Here, we focus on design of optical holograms for generation of holographic images at multiple observation planes and colors via a deep learning model, the CHoloNet. The CHoloNet produces optical holograms which show multitasking performance as multiplexing color holographic image planes by tuning holographic structures. Furthermore, our deep learning model retrieves an object/hologram information from an intensity holographic image without requiring phase and amplitude information from the intensity image. We show that reconstructed objects/holograms show excellent agreement with the ground-truth images. The CHoloNet does not need iteratively reconstruction of object/hologram information while conventional object/hologram recovery methods rely on multiple holographic images at various observation planes along with the iterative algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of optical holograms, and we believe that the CHoloNet based object/hologram reconstruction and generation of holographic images will speed up wide-area implementation of optical holography in microscopy, data encryption, and communication technologies.

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“…CNNs were used to enhance the sharpness of images to predict the focal plane of any given defocused image, 26 and to predict the focal position of the acquired image without axial scanning 27 . Some used a CNN approach for automatically maintaining focus during brightfield microscopy, 28 while others used CNNs with a regression layer as the top layer to estimate the best reconstruction distance 29,30 . Fourier neural networks were used to predict the focus correction from a single image in a wide range of existing microscopes 31 .…”

Section: Introductionmentioning

confidence: 99%

Establishing a reference focal plane using beads for trypan‐blue‐based viability measurements

Peskin

Lund

Pierce

et al. 2021

Journal of Microscopy

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Trypan blue dye exclusion‐based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal‐to‐noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell‐based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto‐focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single‐shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open‐source Graphical User Interface called Bead Benchmarking‐Focus And Intensity Tool (BB‐FAIT) to implement these methods for a semi‐automated cell viability analyser.

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No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Cited by 31 publications

References 45 publications

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network

Comprehensive deep learning model for 3D color holography

Establishing a reference focal plane using beads for trypan‐blue‐based viability measurements

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