Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics
Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics
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DOI：
10.3390/jimaging4120147
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发表时间：
2018-12-01
影响因子：
3.2
通讯作者：
Maier-Hein, Lena
中科院分区：
文献类型：
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作者：
Groehl, Janek;Kirchner, Thomas;Maier-Hein, Lena
在医疗应用中，成像方法的准确性和稳健性对于确保最佳的患者护理至关重要。虽然光声成像（PAI）是一种具有广阔临床应用前景的新兴模式，但最先进的定量光声成像（qPAI）方法旨在解决从获得的测量结果中恢复光学吸收的不适定逆问题，但目前无法满足这些高标准。这可以归因于这样的事实：现有方法通常依赖于对底层物理组织特性的几个简化的先验假设，或者无法处理现实的噪声水平。在这份手稿中，我们用一种新方法来解决这个问题，该方法用于估计估计光学特性的不确定性指标。具体来说，我们的方法使用深度学习模型来计算 qPAI 算法的光学参数估计的误差估计。功能组织参数（例如血氧饱和度）通常是通过对整个基于信号强度的感兴趣区域 (ROI) 进行平均而得出。因此，我们建议通过另外丢弃我们的方法估计高误差和低置信度的那些像素来减少 ROI 样本的系统误差。计算机实验表明，当应用我们的方法来细化 ROI 时，光学吸收定量的准确性得到了提高，因此它可能成为提高 qPAI 方法稳健性的宝贵工具。
In medical applications, the accuracy and robustness of imaging methods are of crucial importance to ensure optimal patient care. While photoacoustic imaging (PAI) is an emerging modality with promising clinical applicability, state-of-the-art approaches to quantitative photoacoustic imaging (qPAI), which aim to solve the ill-posed inverse problem of recovering optical absorption from the measurements obtained, currently cannot comply with these high standards. This can be attributed to the fact that existing methods often rely on several simplifying a priori assumptions of the underlying physical tissue properties or cannot deal with realistic noise levels. In this manuscript, we address this issue with a new method for estimating an indicator of the uncertainty of an estimated optical property. Specifically, our method uses a deep learning model to compute error estimates for optical parameter estimations of a qPAI algorithm. Functional tissue parameters, such as blood oxygen saturation, are usually derived by averaging over entire signal intensity-based regions of interest (ROIs). Therefore, we propose to reduce the systematic error of the ROI samples by additionally discarding those pixels for which our method estimates a high error and thus a low confidence. In silico experiments show an improvement in the accuracy of optical absorption quantification when applying our method to refine the ROI, and it might thus become a valuable tool for increasing the robustness of qPAI methods.