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arXiv · 2403.00764

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model

Thitiya Seesan·Pradipta Mukherjee·Ibrahim Abd El-Sadek·Yiheng Lim·Lida Zhu·Shuichi Makita·Yoshiaki Yasuno
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Abstract

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

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BibTeXRIS

Thitiya Seesan, Pradipta Mukherjee, Ibrahim Abd El-Sadek, Yiheng Lim, Lida Zhu, Shuichi Makita, Yoshiaki Yasuno. 2024-01-23. Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model. https://doi.org/10.1364/boe.519743

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