Abstract

Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin–Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation. This paper reports on extensions to preliminary work published earlier as one of the first applications of deep learning in the field of digital holographic microscopy.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network

Keyvan Jaferzadeh, Seung-Hyeon Hwang, Inkyu Moon, and Bahram Javidi
Biomed. Opt. Express 10(8) 4276-4289 (2019)

Digital holographic particle volume reconstruction using a deep neural network

Tomoyoshi Shimobaba, Takayuki Takahashi, Yota Yamamoto, Yutaka Endo, Atsushi Shiraki, Takashi Nishitsuji, Naoto Hoshikawa, Takashi Kakue, and Tomoyosh Ito
Appl. Opt. 58(8) 1900-1906 (2019)

Accurate detection of small particles in digital holography using fully convolutional networks

Xuecheng Wu, Xinwen Li, Longchao Yao, Yingchun Wu, Xiaodan Lin, Linghong Chen, and Kefa Cen
Appl. Opt. 58(34) G332-G344 (2019)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (10)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (3)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription