Deep learning framework applied to optical diffraction tomography (ODT)

2021 ◽  
Author(s):  
William Pierré ◽  
Lionel Hervé ◽  
Cédric Allier ◽  
Sophie Morales ◽  
Sergei Grudinin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Learning Framework ◽  
Optical Diffraction Tomography

scholarly journals Deep-learning projector for optical diffraction tomography

2020 ◽  
Vol 28 (3) ◽  
pp. 3905 ◽  
Author(s):  
Fangshu Yang ◽  
Thanh-an Pham ◽  
Harshit Gupta ◽  
Michael Unser ◽  
Jianwei Ma
Keyword(s):  
Deep Learning ◽  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography

scholarly journals Deep learning-based optical field screening for robust optical diffraction tomography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
DongHun Ryu ◽  
YoungJu Jo ◽  
Jihyeong Yoo ◽  
Taean Chang ◽  
Daewoong Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tomographic Reconstruction ◽  
Saliency Map ◽  
Optical Field ◽  
Optical Diffraction ◽  
Training Dataset ◽  
Test Accuracy ◽  
Diffraction Tomography ◽  
Rule Based ◽  
Optical Diffraction Tomography

Abstract In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distil the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary-classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with >90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model’s performance and generalisability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualisation techniques that provided the saliency map underlying each model inference. We envision the proposed network would a powerful lightweight module in the tomographic reconstruction pipeline.

scholarly journals Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography

2019 ◽  
Vol 27 (4) ◽  
pp. 4927 ◽  
Author(s):  
Gunho Choi ◽  
DongHun Ryu ◽  
YoungJu Jo ◽  
Young Seo Kim ◽  
Weisun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Optical Diffraction ◽  
Learning Approach ◽  
Diffraction Tomography ◽  
Coherent Noise ◽  
Optical Diffraction Tomography

scholarly journals Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms

2018 ◽  
Author(s):  
Jimin Lee ◽  
Hyejin Kim ◽  
Hyungjoo Cho ◽  
YoungJu Jo ◽  
Yujin Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescent Proteins ◽  
Time Lapse ◽  
Optical Diffraction ◽  
Live Cells ◽  
Label Free ◽  
Cell Nuclei ◽  
Learning Framework ◽  
Optical Diffraction Tomography ◽  
Exogenous Agents

AbstractIn order to identify cell nuclei, fluorescent proteins or staining agents has been widely used. However, use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis, and even interferes with intrinsic physiological conditions. In this work, we proposed a method of label-free segmentation of cell nuclei in optical diffraction tomography images by exploiting a deep learning framework. The proposed method was applied for precise cell nucleus segmentation in two, three, and four-dimensional label-free imaging. A novel architecture with optimised training strategies was validated through cross-modality and cross-laboratory experiments. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.

Total-shear grating based optical diffraction tomography

2021 ◽  
Author(s):  
Piotr Zdańkowski ◽  
Julianna Winnik ◽  
Paweł Gocłowski ◽  
Maciej Trusiak
Keyword(s):  
Optical Diffraction ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography ◽  
Total Shear
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