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

Isotropic quantitative differential phase contrast microscopy using deep neural networks

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Biomedical Imaging and Sensing Conference 2020
Editors	Toyohiko Yatagai, Yoshihisa Aizu, Osamu Matoba, Yasuhiro Awatsuji, Yuan Luo
Publisher	SPIE
ISBN (Electronic)	9781510638495
DOIs	https://doi.org/10.1117/12.2573292
State	Published - 2020
Externally published	Yes
Event	6th Biomedical Imaging and Sensing Conference - Yokohama, Japan Duration: 20 04 2020 → 24 04 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11521
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	6th Biomedical Imaging and Sensing Conference
Country/Territory	Japan
City	Yokohama
Period	20/04/20 → 24/04/20

Bibliographical note

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Keywords

Deep neural network
Isotropic quantitative differential phase contrast microscopy
Patch-based model
Phase retrieval

Access to Document

10.1117/12.2573292

Cite this

Li, A. C., Lin, Y. H., Huang, H. M., & Luo, Y. (2020). Isotropic quantitative differential phase contrast microscopy using deep neural networks. In T. Yatagai, Y. Aizu, O. Matoba, Y. Awatsuji, & Y. Luo (Eds.), Biomedical Imaging and Sensing Conference 2020 Article 1152110 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11521). SPIE. https://doi.org/10.1117/12.2573292

Li, An Cin ; Lin, Yu Hsiang ; Huang, Hsuan Ming et al. / Isotropic quantitative differential phase contrast microscopy using deep neural networks. Biomedical Imaging and Sensing Conference 2020. editor / Toyohiko Yatagai ; Yoshihisa Aizu ; Osamu Matoba ; Yasuhiro Awatsuji ; Yuan Luo. SPIE, 2020. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{ce98b8e7a242430f9a87ca56abaafd4a,

title = "Isotropic quantitative differential phase contrast microscopy using deep neural networks",

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.",

keywords = "Deep neural network, Isotropic quantitative differential phase contrast microscopy, Patch-based model, Phase retrieval",

author = "Li, {An Cin} and Lin, {Yu Hsiang} and Huang, {Hsuan Ming} and Yuan Luo",

note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 6th Biomedical Imaging and Sensing Conference ; Conference date: 20-04-2020 Through 24-04-2020",

year = "2020",

doi = "10.1117/12.2573292",

language = "英语",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Toyohiko Yatagai and Yoshihisa Aizu and Osamu Matoba and Yasuhiro Awatsuji and Yuan Luo",

booktitle = "Biomedical Imaging and Sensing Conference 2020",

address = "美国",

}

Li, AC, Lin, YH, Huang, HM & Luo, Y 2020, Isotropic quantitative differential phase contrast microscopy using deep neural networks. in T Yatagai, Y Aizu, O Matoba, Y Awatsuji & Y Luo (eds), Biomedical Imaging and Sensing Conference 2020., 1152110, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11521, SPIE, 6th Biomedical Imaging and Sensing Conference, Yokohama, Japan, 20/04/20. https://doi.org/10.1117/12.2573292

Isotropic quantitative differential phase contrast microscopy using deep neural networks. / Li, An Cin; Lin, Yu Hsiang; Huang, Hsuan Ming et al.
Biomedical Imaging and Sensing Conference 2020. ed. / Toyohiko Yatagai; Yoshihisa Aizu; Osamu Matoba; Yasuhiro Awatsuji; Yuan Luo. SPIE, 2020. 1152110 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11521).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Isotropic quantitative differential phase contrast microscopy using deep neural networks

AU - Li, An Cin

AU - Lin, Yu Hsiang

AU - Huang, Hsuan Ming

AU - Luo, Yuan

N1 - Publisher Copyright: © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Deep neural network

KW - Isotropic quantitative differential phase contrast microscopy

KW - Patch-based model

KW - Phase retrieval

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T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Biomedical Imaging and Sensing Conference 2020

A2 - Yatagai, Toyohiko

A2 - Aizu, Yoshihisa

A2 - Matoba, Osamu

A2 - Awatsuji, Yasuhiro

A2 - Luo, Yuan

PB - SPIE

T2 - 6th Biomedical Imaging and Sensing Conference

Y2 - 20 April 2020 through 24 April 2020

ER -

Isotropic quantitative differential phase contrast microscopy using deep neural networks

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Keywords

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