@inproceedings{2bd136c244e5449baca32066afcf2bfa,
title = "Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients",
abstract = "Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.",
keywords = "Brain tumor, Cancer surgery, Classifier, Convolutional neural network (CNN), Deep learning, Hyperspectral imaging, Intraoperative imaging, Supervised classification",
author = "Himar Fabelo and Martin Halicek and Samuel Ortega and Adam Szolna and Jesus Morera and Roberto Sarmiento and Callico, {Gustavo M.} and Baowei Fei",
note = "Funding Information: This research was supported in part by the U.S. National Institutes of Health (NIH) grants (R21CA176684, R01CA156775, R01CA204254, and R01HL140325). In addition, this work was 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 also was 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 was 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 and This work was completed while Samuel Ortega was beneficiary of a predoctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Informaci{\'o}n (ACIISI)” of the “Conserjer{\'i}a de Econom{\'i}a, 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%)). Publisher Copyright: {\textcopyright} 2019 SPIE.; Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 17-02-2019 Through 19-02-2019",
year = "2019",
doi = "10.1117/12.2512569",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2019",
}