%0 Journal Article
%A Bench, C
%A Hauptmann, A
%A Cox, B
%D 2020
%F discovery:10108541
%J Journal of Biomedical Optics
%K photoacoustics; deep learning; oxygen saturation; sO2; machine learning; quantitative photoacoustics.
%N 8
%T Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions
%U https://discovery.ucl.ac.uk/id/eprint/10108541/
%V 25
%X 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.
%Z 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