High-speed optical diffraction tomography (ODT) with deep-learning approach (Conference Presentation)

2020 ◽  
Author(s):  
Baoliang Ge ◽  
Mo Deng ◽  
George Barbastathis ◽  
Peter T. C. So ◽  
Renjie Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Optical Diffraction ◽  
Learning Approach ◽  
Diffraction Tomography ◽  
Optical Diffraction Tomography

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 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.

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