Deep-Learning-Based Virtual H&amp;E Staining Using Total-Absorption Photoacoustic Remote Sensing (TA-PARS)

Marian Boktor; Benjamin Ecclestone; Benjamin Ecclestone; Vlad Pekar; Vlad Pekar; Deepak Dinakaran; John R. Mackey; Paul Fieguth; Parsin Haji Reza

doi:10.1364/MICROSCOPY.2022.MS4A.3

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper MS4A.3
•https://doi.org/10.1364/MICROSCOPY.2022.MS4A.3

Deep-Learning-Based Virtual H&E Staining Using Total-Absorption Photoacoustic Remote Sensing (TA-PARS)

Marian Boktor, Benjamin Ecclestone, Vlad Pekar, Deepak Dinakaran, John R. Mackey, Paul Fieguth, and Parsin Haji Reza

Microscopy Histopathology and Analytics 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

From the session
Deep Learning and Computational Pathology (MS4A)

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
M. Boktor, B. Ecclestone, V. Pekar, D. Dinakaran, J. R. Mackey, P. Fieguth, and P. H. Reza, "Deep-Learning-Based Virtual H&E Staining Using Total-Absorption Photoacoustic Remote Sensing (TA-PARS)," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper MS4A.3.

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Get Video
Citation alert
Save article

Abstract

Deep learning-based virtual staining is applied to total-absorption photoacoustic remote sensing (TA-PARS) imaging, demonstrating virtual histological images with equivalent quality and contrast to histochemical staining.

PDF Article | Presentation Video

More Like This

Label-Free Histology with Total Absorption Photoacoustic Remote Sensing (TA-PARS)

Benjamin Ecclestone, Kevan Bell, Marian Boktor, Vlad Pekar, Deepak Dinakaran, John R. Mackey, and Parsin Haji Reza
MTu2A.4 Microscopy Histopathology and Analytics (Microscopy) 2022

Deep learning-based transformation of H&E stained tissue into special stains

Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman, Tairan Liu, Yair Rivenson, W. Dean Wallace, and Aydogan Ozcan
ATh2I.4 CLEO: Applications and Technology (CLEO:A&T) 2022

Multi-layer label-free H&E-like histology using ultraviolet scattering-augmented photoacoustic remote sensing microscopy

Nathaniel J.M. Haven, Matthew T. Martell, Brendyn D. Cikaluk, Brendon S. Restall, Ewan McAlister, Sveta Silverman, Lashan Peiris, Jean Deschenes, Xingyu Li, and Roger J. Zemp
MTu2A.2 Microscopy Histopathology and Analytics (Microscopy) 2022

Previous Article Next Article

Presentation Video

Presentation video access is available to:

Optica Publishing Group subscribers
Technical meeting attendees
Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads