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首页> 外文会议>Conference on optical interactions with tissue and cells >Regression models for real-time estimation of optical and structural sample properties from subdiffusive spatially resolved reflectance
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Regression models for real-time estimation of optical and structural sample properties from subdiffusive spatially resolved reflectance

机译：利用亚扩散空间分辨反射率实时估算光学和结构样品特性的回归模型

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Timely estimation of optical properties from spatially resolved reflectance is a challenging task since the inverse light propagation model needs to be evaluated in real time. In this paper, we propose and extensively evaluate artificial neural network based regression model for estimation of optical and structural sample properties from spatially resolved reflectance acquired by optical fiber probes. We show that the proposed regression model can be prepared from datasets of Monte Carlo simulated spatially resolved reflectance and evaluated significantly faster than the frequently used dense lookup table inverse model. We observed computation time improvements exceeding 4 orders of magnitude. Moreover, the regression model can be easily extended to estimate more free parameters without reducing the estimation accuracy. Finally, we utilized the proposed regression model to estimate optical properties of human skin subjected to dynamically changing contact pressure applied by an optical fiber probe.
机译：由于需要实时评估逆光传播模型，因此根据空间分辨的反射率及时估算光学特性是一项艰巨的任务。在本文中，我们提出并广泛评估了基于人工神经网络的回归模型，用于从光纤探头获得的空间分辨反射率中估算光学和结构样品的特性。我们表明，所提出的回归模型可以从蒙特卡洛模拟的空间分辨反射率的数据集中准备，并且比常用的密集查找表逆模型要快得多。我们观察到计算时间的改进超过了4个数量级。此外，可以轻松扩展回归模型以估计更多自由参数，而不会降低估计精度。最后，我们利用所提出的回归模型来估计经受由光纤探针施加的动态变化的接触压力的人体皮肤的光学特性。

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