Ciaran Bench, Andreas Hauptmann, and Ben T. Cox "Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions," Journal of Biomedical Optics 25(8), 085003 (24 August 2020). https://doi.org/10.1117/1.JBO.25.8.085003
Toward accurate quantitative photoacoustic imaging : learning vascular blood oxygen saturation in three dimensions
|Author:||Bench, Ciaran1; Hauptmann, Andreas2,3; Cox, Ben1|
1University College London, Department of Medical Physics and Biomedical Engineering, Gower Street, London, United Kingdom
2University of Oulu, Research Unit of Mathematical Sciences, Oulu, Finland
3University College London, Department of Computer Science, Gower Street, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020082663168
|Publish Date:|| 2020-08-26
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.
Journal of biomedical optics
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
114 Physical sciences
217 Medical engineering
The authors acknowledge support from the BBSRC London Interdisciplinary Doctoral Programme, LIDo, the European Union’s Horizon 2020 research, and innovation program H2020 ICT 2016-2017 under Grant Agreement No. 732411, which is an initiative of the Photonics Public Private Partnership, the Academy of Finland Project 312123 (Finnish Centre of Excellence in Inverse Modelling and Imaging, 2018–2025), and the CMIC-EPSRC platform Grant (EP/M020533/1).
|Academy of Finland Grant Number:||
312123 (Academy of Finland Funding decision)
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