TY - JOUR AV - public IS - 8 N2 - Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo. KW - photoacoustics; deep learning; oxygen saturation; sO2; machine learning; quantitative photoacoustics. A1 - Bench, C A1 - Hauptmann, A A1 - Cox, B VL - 25 JF - Journal of Biomedical Optics N1 - Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI TI - Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions Y1 - 2020/08/24/ UR - https://doi.org/10.1117/1.JBO.25.8.085003 ID - discovery10108541 ER -