Presentation
9 March 2023 Toward automated deep learning-based virtual histological staining of slide-free total absorption photoacoustic remote sensing (TA-PARS)
Author Affiliations +
Abstract
Significant efforts are being made to reduce histology turnaround times. Total-Absorption Photoacoustic Remote Sensing (TA-PARS) is the first independent all-optical, label-free optical microscope to provide radiative and non-radiative absorption and optical scattering in a single acquisition. Such array of contrasts enables TA-PARS to rapidly capture most diagnostic elements. Here, a deep learning model, Pix2Pix, is trained within an end-to-end virtual staining framework, utilizing such contrasts. Virtually stained thin and fresh tissue exhibit high concordance when compared against histochemical staining. The proposed work paves the way for developing TA-PARS slide-free histology, which may revolutionize intraoperative microscopic diagnosis and margin assessment.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marian Boktor, Benjamin R. Ecclestone, Vlad Pekar, Deepak Dinakaran, John R. Mackey, Paul Fieguth, and Parsin Haji Reza "Toward automated deep learning-based virtual histological staining of slide-free total absorption photoacoustic remote sensing (TA-PARS)", Proc. SPIE PC12379, Photons Plus Ultrasound: Imaging and Sensing 2023, PC123791I (9 March 2023); https://doi.org/10.1117/12.2650307
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KEYWORDS
Absorption

Photoacoustic spectroscopy

Remote sensing

Tissues

Optical microscopes

Chemical elements

Gold

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