Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/58284
Title: Deep learning-based framework for In Vivo identification of glioblastoma tumor using hyperspectral images of human brain
Authors: Fabelo Gómez, Himar Antonio 
Halicek, Martin
Ortega Sarmiento, Samuel 
Shahedi, Maysam
Zbigniew Szolna,Adam 
Piñeiro, Juan F.
Sosa Pérez, Coralia 
J-O’Shanahan, Aruma
Bisshopp, Sara
Espino, Carlos
Márquez, Mariano
Hernández, María
Carrera, David
Morera Molina, Jesús Manuel 
Marrero Callicó, Gustavo Iván 
Sarmiento Rodríguez, Roberto 
Fei, Baowei
UNESCO Clasification: 3314 Tecnología médica
Keywords: Brain tumor
Cancer surgery
Hyperspectral imaging
Bioinformatics
Intraoperative imaging
Deep learning
Precision medicine
Image-guided surgery
Issue Date: 2019
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca) 
Plataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales 
Hyperspectral Imaging Cancer Detection (Helicoid) (Contrato Nº 618080) 
Journal: Sensors 
Abstract: The 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.
URI: http://hdl.handle.net/10553/58284
ISSN: 1424-8220
DOI: 10.3390/s19040920
Source: Sensors [ISSN 1424-8220], v. 19 (4), artículo 920
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