@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 = "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",
}