Improving the estimation accuracy of the scatterer density estimation by accounting for the spatial property of the noise in optical coherence tomography

Thitiya Seesan; Pradipta Mukherjee; Ibrahim Abd El-Sadek; Yiheng Lim; Shuichi Makita; Prathan Buranasiri; Yoshiaki Yasuno

doi:10.1117/12.2648798

9 March 2023 Improving the estimation accuracy of the scatterer density estimation by accounting for the spatial property of the noise in optical coherence tomography

Thitiya Seesan, Pradipta Mukherjee, Ibrahim Abd El-Sadek, Yiheng Lim, Shuichi Makita, Prathan Buranasiri, Yoshiaki Yasuno

Author Affiliations +

Proceedings Volume PC12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; PC123670Z (2023) https://doi.org/10.1117/12.2648798
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

A new deep-learning-based scatterer density estimator (SDE) is demonstrated. The SDE is trained by pairs of numerically simulated OCT images and its background parameters including the scatterer density, resolutions, and signal-to-noise ratio. For this simulation, we introduced a new noise model that accurately accounts for the spatial properties of three noise types: shot, relative-intensity, and detector noise. This SDE was experimentally validated by phantom and in-vitro tumor spheroid measurements. Significantly improved accuracy was found in comparison to our old SDE being trained with a naïve noise model that does not account for the spatial noise property.

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Thitiya Seesan, Pradipta Mukherjee, Ibrahim Abd El-Sadek, Yiheng Lim, Shuichi Makita, Prathan Buranasiri, and Yoshiaki Yasuno "Improving the estimation accuracy of the scatterer density estimation by accounting for the spatial property of the noise in optical coherence tomography", Proc. SPIE PC12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, PC123670Z (9 March 2023); https://doi.org/10.1117/12.2648798

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