A Machine Learning Approach to Determining Sub-Diffuse Optical Properties

Autor:	Andrew C. Stier, Mia K. Markey, Jason S. Reichenberg, Will Goth, Matthew C. Fox, Yao Zhang, James W. Tunnell, Fabiana C.P.S. Lopes, Katherine R. Sebastian
Rok vydání:	2020
Předmět:	Computer science business.industry Mie scattering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Optical property Foundation (engineering) Machine learning computer.software_genre Light scattering Domain (software engineering) Artificial intelligence Spatial frequency business computer
Zdroj:	Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN).
DOI:	10.1364/ots.2020.sm2d.6
Popis:	We demonstrate a machine learning approach that renders real-time subdiffuse optical property (γ) maps from spatial frequency domain images, laying the foundation for use in surgical guidance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::412e53c2755e52c0af75974f84864270 https://doi.org/10.1364/ots.2020.sm2d.6 Zobrazit plný text záznamu