Isotropic quantitative differential phase contrast microscopy using deep neural networks

An-Cin Li; Yu-Hsiang Lin; Hsuan-Ming Huang; Yuan Luo

doi:10.1117/12.2573292

15 June 2020 Isotropic quantitative differential phase contrast microscopy using deep neural networks

An-Cin Li, Yu-Hsiang Lin, Hsuan-Ming Huang, Yuan Luo

Proceedings Volume 11521, Biomedical Imaging and Sensing Conference 2020; 1152110 (2020) https://doi.org/10.1117/12.2573292
Event: SPIE Technologies and Applications of Structured Light, 2020, Yokohama, Japan

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.

Citation Download Citation

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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