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首页> 外文会议>SPIE 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：来自沉屈的空间分辨反射率的光学和结构样本性能实时估计的回归模型

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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：及时估计来自空间分辨反射的光学性质是一个具有挑战性的任务，因为需要实时评估逆光传播模型。在本文中，我们提出并广泛地评估了用于估计光纤探针的空间分辨反射的光学和结构样本的基于人工神经网络的回归模型。我们表明，所提出的回归模型可以从Monte Carlo的数据集进行模拟空间解决的反射率，并比常用密集查找表逆模型更快地评估。我们观察到计算时间超过4个级别的改进。此外，回归模型可以很容易地扩展以估计更多的自由参数而不降低估计精度。最后，我们利用所提出的回归模型来估计人体皮肤的光学性质，所述人体皮肤经受动态地改变光纤探针施加的接触压力。

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