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J. Imaging 2018, 4(12), 147; https://doi.org/10.3390/jimaging4120147

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

1
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
2
Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
3
Faculty of Physics and Astronomy, Heidelberg University, 69120 Heidelberg, Germany
*
Authors to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 2 December 2018 / Accepted: 6 December 2018 / Published: 10 December 2018
(This article belongs to the Special Issue Biomedical Photoacoustic Imaging: Technologies and Methods)
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Abstract

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. View Full-Text
Keywords: confidence learning; uncertainty estimation; quantitative photoacoustic imaging; error analysis; deep learning confidence learning; uncertainty estimation; quantitative photoacoustic imaging; error analysis; deep learning
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Gröhl, J.; Kirchner, T.; Adler, T.; Maier-Hein, L. Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics. J. Imaging 2018, 4, 147.

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