Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69251
Title: Towards Real-Time Computing of Intraoperative Hyperspectral Imaging for Brain Cancer Detection Using Multi-GPU Platforms
Authors: Florimbi, Giordana
Fabelo Gómez, Himar Antonio 
Torti, Emanuele
Ortega Sarmiento, Samuel 
Marrero Martín, Margarita Luisa 
Marrero Callicó, Gustavo Iván 
Danese, Giovanni
Leporati, Francesco
UNESCO Clasification: 3314 Tecnología médica
Keywords: Hyperspectral imaging
High performance computing
Graphic processing unit
Parallel processing
Parallel architectures, et al
Issue Date: 2020
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: IEEE Access 
Abstract: Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons’ naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient’s quality of life. The brain cancer detection system developed as part of the “HypErspectraL Imaging Cancer Detection” (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future.
URI: http://hdl.handle.net/10553/69251
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2963939
Source: IEEE Access [ISSN 2169-3536], v. 8, p. 8485 - 8501
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