eprintid: 10043902
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/04/39/02
datestamp: 2018-02-22 12:32:30
lastmod: 2021-09-17 22:07:23
status_changed: 2018-02-22 12:32:30
type: article
metadata_visibility: show
creators_name: Fabelo, H
creators_name: Ortega, S
creators_name: Lazcano, R
creators_name: Madroñal, D
creators_name: Callicó, GM
creators_name: Juárez, E
creators_name: Salvador, R
creators_name: Bulters, D
creators_name: Bulstrode, H
creators_name: Szolna, A
creators_name: Piñeiro, JF
creators_name: Sosa, C
creators_name: O Shanahan, AJ
creators_name: Bisshopp, S
creators_name: Hernández, M
creators_name: Morera, J
creators_name: Ravi, D
creators_name: Kiran, BR
creators_name: Vega, A
creators_name: Báez-Quevedo, A
creators_name: Yang, GZ
creators_name: Stanciulescu, B
creators_name: Sarmiento, R
title: An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Hyperspectral imaging instrumentation; brain cancer detection; image processing
note: This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
abstract: Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.
date: 2018-02-01
date_type: published
official_url: https://doi.org/10.3390/s18020430
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1537572
doi: 10.3390/s18020430
lyricists_name: Ravi, Daniele
lyricists_id: DRAVI97
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: Sensors
volume: 18
number: 2
article_number: 430
issn: 1424-8220
citation:        Fabelo, H;    Ortega, S;    Lazcano, R;    Madroñal, D;    Callicó, GM;    Juárez, E;    Salvador, R;                                                                 ... Sarmiento, R; + view all <#>        Fabelo, H;  Ortega, S;  Lazcano, R;  Madroñal, D;  Callicó, GM;  Juárez, E;  Salvador, R;  Bulters, D;  Bulstrode, H;  Szolna, A;  Piñeiro, JF;  Sosa, C;  O Shanahan, AJ;  Bisshopp, S;  Hernández, M;  Morera, J;  Ravi, D;  Kiran, BR;  Vega, A;  Báez-Quevedo, A;  Yang, GZ;  Stanciulescu, B;  Sarmiento, R;   - view fewer <#>    (2018)    An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation.                   Sensors , 18  (2)    , Article 430.  10.3390/s18020430 <https://doi.org/10.3390/s18020430>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10043902/1/sensors-18-00430.pdf