Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor Using Hyperspectral Images of Human Brain

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dc.contributor.ORCID0000-0002-9794-490X (Fabelo, H)
dc.contributor.ORCID0000-0002-9123-9484 (Fei, B)
dc.contributor.authorFabelo, Himar
dc.contributor.authorHalicek, Martin
dc.contributor.authorOrtega, S.
dc.contributor.authorShahedi, Maysam
dc.contributor.authorSzolna, A.
dc.contributor.authorPiñeiro, J. F.
dc.contributor.authorSosa, C.
dc.contributor.authorO’Shanahan, A. J.
dc.contributor.authorBisshopp, S.
dc.contributor.authorEspino, C.
dc.contributor.authorMárquez, M.
dc.contributor.authorHernández, M.
dc.contributor.authorCarrera, D.
dc.contributor.authorMorera, J.
dc.contributor.authorCallico, G. M.
dc.contributor.authorSarmiento, R.
dc.contributor.authorFei, Baowei
dc.contributor.utdAuthorFabelo, Himar
dc.contributor.utdAuthorHalicek, Martin
dc.contributor.utdAuthorShahedi, Maysam
dc.contributor.utdAuthorFei, Baowei
dc.date.accessioned2020-02-07T23:15:05Z
dc.date.available2020-02-07T23:15:05Z
dc.date.created2019-02-22
dc.descriptionIncludes supplementary information
dc.description.abstractThe main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorship"This research was supported in part by the U.S. National Institutes of Health (NIH) grants (R21CA176684, R01CA156775, R01CA204254, and R01HL140325). Also, this work has been supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperspectral Identification of Brain Tumors” under Grant Agreement ProID2017010164 and it has been partially supported also by the Spanish Government and European Union (FEDER funds) as part of support program in the context of Distributed HW/SW Platform for Intelligent Processing of Heterogeneous Sensor Data in Large Open Areas Surveillance Applications (PLATINO) project, under contract TEC2017-86722-C4-1-R. This work has been also supported in part by the European Commission through the FP7 FET (Future Emerging Technologies) Open Programme ICT-2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant Agreement 618080. Additionally, this work has been supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria. Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%))."
dc.identifier.bibliographicCitationFabelo, H., M. Halicek, S. Ortega, M. Shahedi, et al. 2019. "Deep learning-based framework for In Vivo identification of glioblastoma tumor using hyperspectral images of human brain." Sensors (Switzerland) 19: art. 928, doi: 10.3390/s19040920.
dc.identifier.issn1424-8220
dc.identifier.urihttp://dx.doi.org/10.3390/s19040920
dc.identifier.urihttps://hdl.handle.net/10735.1/7252
dc.identifier.volume19
dc.language.isoen
dc.publisherMDPI Ag
dc.rightsCC BY 4.0 (Attribution)
dc.rights©2019 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.journalSensors (switzerland)
dc.source.journalSensors (switzerland)
dc.subjectBioinformatics
dc.subjectBrain Neoplasms
dc.subjectBrain--Cancer--Surgery
dc.subjectHyperspectral imaging
dc.subjectIntraoperative imaging
dc.subjectPrecision Medicine
dc.subjectComputer-assisted surgery
dc.titleDeep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor Using Hyperspectral Images of Human Brain
dc.title.alternativeSensors (switzerland)
dc.type.genrearticle

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