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Bayesian Estimation of Intrinsic Tissue Oxygenation and Perfusion from RGB Images

Jones, G; Clancy, N; Helo, Y; Arridge, S; Elson, D; Stoyanov, D; (2017) Bayesian Estimation of Intrinsic Tissue Oxygenation and Perfusion from RGB Images. IEEE Transactions on Medical Imaging 10.1109/TMI.2017.2665627. (In press). Green open access

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Abstract

Multispectral imaging (MSI) can potentially assist the intra-operative assessment of tissue structure, function and viability, by providing information about oxygenation. In this paper, we present a novel technique for recovering intrinsic MSI measurements from endoscopic RGB images without custom hardware adaptations. The advantage of this approach is that it requires no modification to existing surgical and diagnostic endoscopic imaging systems. Our method uses a radiometric colour calibration of the endoscopic camera's sensor in conjunction with a Bayesian framework to recover a per-pixel measurement of the total blood volume (THb) and oxygen saturation (SO2) in the observed tissue. The sensor's pixel measurements are modelled as weighted sums over a mixture of Poisson distributions and we optimise the variables SO2 and THb to maximise the likelihood of the observations. To validate our technique, we use synthetic images generated from Monte Carlo (MC) physics simulation of light transport through soft tissue containing sub-surface blood vessels. We also validate our method on in vivo data by comparing it to a MSI dataset acquired with a hardware system that sequentially images multiple spectral bands without overlap. Our results are promising and show that we are able to provide surgeons with additional relevant information by processing endoscopic images with our modelling and inference framework.

Type: Article
Title: Bayesian Estimation of Intrinsic Tissue Oxygenation and Perfusion from RGB Images
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2017.2665627
Publisher version: http://dx.doi.org/10.1109/TMI.2017.2665627
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1544791
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