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Status: Bibliographieeintrag

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Verfasst von:Wirkert, Sebastian [VerfasserIn]   i
 Kenngott, Hannes Götz [VerfasserIn]   i
 Mayer, Benjamin [VerfasserIn]   i
 Mietkowski, Patrick [VerfasserIn]   i
 Wagner, Martin [VerfasserIn]   i
 Sauer, Peter [VerfasserIn]   i
Titel:Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression
Verf.angabe:Sebastian J. Wirkert, Hannes Kenngott, Benjamin Mayer, Patrick Mietkowski, Martin Wagner, Peter Sauer, Neil T. Clancy, Daniel S. Elson, Lena Maier-Hein
E-Jahr:2016
Jahr:3 May 2016
Umfang:9 S.
Fussnoten:Gesehen am 13.08.2019
Titel Quelle:Enthalten in: International journal of computer assisted radiology and surgery
Ort Quelle:Berlin : Springer, 2006
Jahr Quelle:2016
Band/Heft Quelle:11(2016), 6, Seite 909-917
ISSN Quelle:1861-6429
Abstract:PurposeMultispectral imaging can provide reflectance measurements at multiple spectral bands for each image pixel. These measurements can be used for estimation of important physiological parameters, such as oxygenation, which can provide indicators for the success of surgical treatment or the presence of abnormal tissue. The goal of this work was to develop a method to estimate physiological parameters in an accurate and rapid manner suited for modern high-resolution laparoscopic images.MethodsWhile previous methods for oxygenation estimation are based on either simple linear methods or complex model-based approaches exclusively suited for off-line processing, we propose a new approach that combines the high accuracy of model-based approaches with the speed and robustness of modern machine learning methods. Our concept is based on training random forest regressors using reflectance spectra generated with Monte Carlo simulations.ResultsAccording to extensive in silico and in vivo experiments, the method features higher accuracy and robustness than state-of-the-art online methods and is orders of magnitude faster than other nonlinear regression based methods.ConclusionOur current implementation allows for near real-time oxygenation estimation from megapixel multispectral images and is thus well suited for online tissue analysis.
DOI:doi:10.1007/s11548-016-1376-5
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1007/s11548-016-1376-5
 DOI: https://doi.org/10.1007/s11548-016-1376-5
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Anastomosis
 Inverse Monte Carlo
 Multispectral imaging
 Oxygenation
 Perfusion
 Random forest
 Regression
K10plus-PPN:1671243854
Verknüpfungen:→ Zeitschrift

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