Real-time, wide-field endoscopic quantitative imaging based on 3D profile corrected deep learning SSOP

Luca Baratelli; Enagnon Aguénounon; Manuel Flury; Sylvain Gioux

doi:10.1117/12.2577498

5 March 2021 Real-time, wide-field endoscopic quantitative imaging based on 3D profile corrected deep learning SSOP

Luca Baratelli, Enagnon Aguénounon, Manuel Flury, Sylvain Gioux

Proceedings Volume 11625, Molecular-Guided Surgery: Molecules, Devices, and Applications VII; 116250B (2021) https://doi.org/10.1117/12.2577498
Event: SPIE BiOS, 2021, Online Only

Abstract

The lack of quantitative information in image guided surgery determines still nowadays an unmet clinical need, leading to subjective assessments and variable outcomes. In this framework, we present the design of an endoscopic imaging system and the application of deep learning algorithms for real-time quantitation of tissues optical properties. The instrument is based on deep learning-optimized 3D profile corrected “Single Snapshot imaging of Optical Properties”(3D-SSOP). A first benchtop prototype has been validated on tissue mimicking phantoms and is currently being integrated on a surgical robot for pre-clinical trials on small animals.

Conference Presentation

Citation Download Citation

Luca Baratelli, Enagnon Aguénounon, Manuel Flury, and Sylvain Gioux "Real-time, wide-field endoscopic quantitative imaging based on 3D profile corrected deep learning SSOP", Proc. SPIE 11625, Molecular-Guided Surgery: Molecules, Devices, and Applications VII, 116250B (5 March 2021); https://doi.org/10.1117/12.2577498

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