Paper
15 June 2020 Isotropic quantitative differential phase contrast microscopy using deep neural networks
An-Cin Li, Yu-Hsiang Lin, Hsuan-Ming Huang, Yuan Luo
Author Affiliations +
Abstract
Isotropic quantitative differential phase contrast (iDPC) microscopy based on pupil engineering has made significant improvement in reconstructing phase image of weak phase objects. To further enhance acquisition speed for phase recovery in iDPC, we adapt deep neural networks to achieve isotropic phase retrieval from half-pupil based quantitative differential phase contrast (qDPC) microscopy. We proposed to utilize U-net model for transforming phase distribution from 2-axis reconstruction to 6-axis one. The results show that deep neural network we proposed works as well as we expected. The final loss value of our model after 500 epochs of training can achieve about 5.7e-5 after normalized.
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An-Cin Li, Yu-Hsiang Lin, Hsuan-Ming Huang, and Yuan Luo "Isotropic quantitative differential phase contrast microscopy using deep neural networks", Proc. SPIE 11521, Biomedical Imaging and Sensing Conference 2020, 1152110 (15 June 2020); https://doi.org/10.1117/12.2573292
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KEYWORDS
Microscopy

Neural networks

Phase contrast

Phase retrieval

Phase transfer function

Image retrieval

Image processing

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