Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients

Himar Fabelo, Martin Halicek, Samuel Ortega, Adam Szolna, Jesus Morera, Roberto Sarmiento, Gustavo M. Callico, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
ISBN (Electronic)9781510625495
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 17 2019Feb 19 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10951
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego
Period2/17/192/19/19

Keywords

  • Brain tumor
  • Cancer surgery
  • Classifier
  • Convolutional neural network (CNN)
  • Deep learning
  • Hyperspectral imaging
  • Intraoperative imaging
  • Supervised classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Fabelo, H., Halicek, M., Ortega, S., Szolna, A., Morera, J., Sarmiento, R., Callico, G. M., & Fei, B. (2019). Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling [1095110] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951). SPIE. https://doi.org/10.1117/12.2512569