Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

Seesan, Thitiya; El-Sadek, Ibrahim Abd; Mukherjee, Pradipta; Zhu, Lida; Oikawa, Kensuke; Miyazawa, Akiyoshi; Shen, Larina Tzu-Wei; Matsusaka, Satoshi; Buranasiri, Prathan; Makita, Shuichi; Yasuno, Yoshiaki

doi:10.1364/boe.443343

Cited by 8 publications

(21 citation statements)

References 48 publications

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“…Summary of spatial correlation distance characteristics of three noise types and the OCT signal. In the new noise model, each type of noise has different spatial correlation properties, whereas these properties [18] are identical for all noise types in the old noise model. In this study, we consider scanning OCT, rather than a full-field OCT. Because each A-line of the scanning OCT is acquired at a different time, none of the three noise types have spatial correlation along the lateral direction.…”

Section: Noise Modelmentioning

confidence: 99%

“…Speckle contrast was expected to aid in estimating the number of scatterers in the resolution volume (ENS), while resolution volume could be derived from estimated resolutions (as discussed in Section 4.3 of Ref. [18]). This principle could work effectively if speckle contrast were sensitive to ENS, particularly for small ENS values However, we found that our SDE could function not only in this regime but also for cases with large ENS values.…”

Section: Open Issue: Physical Mechanism Used In the Sdementioning

confidence: 99%

“…Rather than use the AC to assess biological tissue density, we have proposed a deep-learning based method that estimates the scatterer density of the tissue directly [16][17][18]. In this work, we denote this method as the scatterer density estimator (SDE).…”

Section: Introductionmentioning

confidence: 99%

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

Seesan,

Mukherjee,

Abd El-Sadek

et al. 2024

Biomed. Opt. Express

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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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Section: Noise Modelmentioning

confidence: 99%

Section: Open Issue: Physical Mechanism Used In the Sdementioning

confidence: 99%

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

Seesan,

Mukherjee,

Abd El-Sadek

et al. 2024

Biomed. Opt. Express

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“…In our JM-OCT, we additionally use scatterer density imaging achieved by a neural-network-based scatterer density estimator [65]. This estimator is based on a convolutional neural network trained by using numerically generated OCT images.…”

Section: E Intensity Oct Attenuation Coefficient and Scatterer Densitymentioning

confidence: 99%

“…In addition to the D-OCT analysis, Seesan et al recently applied neural-network-based scatterer-density analysis to the JM-OCT signal [65]. This revealed a temporal reduction in the scatterer density after the spheroid was extracted from the culturing environment as shown in Fig.…”

Section: E Ex Vivo and In Vitro Tissue Imagingmentioning

confidence: 99%

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Yasuno

2023

IEEE J. Select. Topics Quantum Electron.

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Jones-matrix optical coherence tomography (JM-OCT) is an extension of polarization-sensitive OCT, which provides multiple types of optical contrasts of biological and clinical samples. JM-OCT measures the spatial distribution of the Jones matrix of the sample and also its time sequence. All contrasts (i.e., multi-contrast OCT images) are then computed from the Jones matrix. The contrasts obtained from the Jones matrix include not only the conventional and polarization-insensitive OCT intensity, cumulative and local phase retardation (birefringence), degreeof-polarization uniformity quantifying the polarization randomness of the sample, diattenuation, but also signal attenuation coefficient, sample scatterer density, Doppler OCT, OCT angiography, and dynamic OCT that contrasts intracellular motility or metabolism by analyzing the temporal fluctuation of the OCT signal. JM-OCT is a generalized version of OCT because it measures the generalized form of the sample information; i.e., the Jones matrix sequence. This review summarizes the basic conception, mathematical principle, hardware implementation, signal and image processing, and biological and clinical applications of JM-OCT. Advanced technical topics, including JM-OCTspecific noise correction and quantity estimation and JM-OCT's self-calibration nature, are also described.

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Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography

Abd El-Sadek,

Morishita,

Mori

et al. 2024

Sci Rep

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We demonstrate label-free dynamic optical coherence tomography (D-OCT)-based visualization and quantitative assessment of patterns of tumor spheroid response to three anti-cancer drugs. The study involved treating human breast adenocarcinoma (MCF-7 cell-line) with paclitaxel (PTX), tamoxifen citrate (TAM), and doxorubicin (DOX) at concentrations of 0 (control), 0.1, 1, and 10 µM for 1, 3, and 6 days. In addition, fluorescence microscopy imaging was performed for reference. The D-OCT imaging was performed using a custom-built OCT device. Two algorithms, namely logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS$$_l$$ l ) were used to visualize the tissue dynamics. The spheroids treated with 0.1 and 1 µM TAM appeared similar to the control spheroid, whereas those treated with 10 µM TAM had significant structural corruption and decreasing LIV and OCDS$$_l$$ l over treatment time. The spheroids treated with PTX had decreasing volumes and decrease of LIV and OCDS$$_l$$ l signals over time at most PTX concentrations. The spheroids treated with DOX had decreasing and increasing volumes over time at DOX concentrations of 1 and 10 µM, respectively. Meanwhile, the LIV and OCDS$$_l$$ l signals decreased over treatment time at all DOX concentrations. The D-OCT, particularly OCDS$$_l$$ l , patterns were consistent with the fluorescence microscopic patterns. The diversity in the structural and D-OCT results among the drug types and among the concentrations are explained by the mechanisms of the drugs. The presented results suggest that D-OCT is useful for evaluating the difference in the tumor spheroid response to different drugs and it can be a useful tool for anti-cancer drug testing.

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Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography

Cited by 8 publications

References 48 publications

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

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

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography

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