Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

Sabir Sohail; Cho Sanghoon; Kim Yejin; Pua Rizza; Heo Duchang; Kim Kee Hyun; Choi Youngwook; Cho Seungryong

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Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

机译：基于卷积神经网络的弥漫性光学断层扫描中的批量光学特性方法

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Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time. (C) 2020 Optical Society of America.

机译：深入学习已经积极调查了图像分类，计算机愿景和回归任务等各种应用，并且它表明了最先进的性能。在漫射光学断层扫描（点）中，介质的批量光学特性的精确估计是至关重要的，因为它直接影响整体图像质量。在这项工作中，我们利用深入学习，提出一种小说，据我们所知，卷积神经网络（CNN）基于估计高度散射介质的批量光学性质，例如DOT中的生物组织。我们通过使用实验和模拟数据验证了所提出的方法。对于绩效评估，我们将建议方法与现有方法进行比较。结果表明，在估计精度方面，所提出的基于CNN的批量光学性能估计方法优于现有方法，计算时间较低。（c）2020美国光学学会。

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《Applied optics》 |2020年第5期|共10页
作者
Sabir Sohail; Cho Sanghoon; Kim Yejin; Pua Rizza; Heo Duchang; Kim Kee Hyun; Choi Youngwook; Cho Seungryong;
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Korea Adv Inst Sci &

Technol Dept Nucl &

Quantum Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Nucl &

Quantum Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Nucl &

Quantum Engn Daejeon 34141 South Korea;

Korea Adv Inst Sci &

Technol Dept Nucl &

Quantum Engn Daejeon 34141 South Korea;

Korea Electrotechnol Res Inst Ansan 15588 South Korea;

Korea Electrotechnol Res Inst Ansan 15588 South Korea;

Korea Electrotechnol Res Inst Ansan 15588 South Korea;

Korea Adv Inst Sci &

Technol Dept Nucl &

Quantum Engn Daejeon 34141 South Korea;

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Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography

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