Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44978
Title: HELICoiD: Interdisciplinary and collaborative project for real-time brain cancer detection
Authors: Salvador, Rubén
Ortega, Samuel 
Madroñal, Daniel
Fabelo, Himar 
Lazcano, Raquel
Marrero Callicó, Gustavo Iván 
Juárez, Eduardo
Sarmiento, Roberto 
Sanz, César
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Imaging techniques
Spectroscopy
Compressive spectral
Issue Date: 2017
Journal: ACM International Conference on Computing Frontiers 2017, CF 2017
Conference: 14th ACM International Conference on Computing Frontiers, CF 2017 
Abstract: The HELICoiD project is a European FP7 FET Open funded project. It is an interdisciplinary work at the edge of the biomedical domain, bringing together neurosurgeons, computer scientists and electronic engineers. The main target of the project was to provide a working demonstrator of an intraoperative image-guided surgery system for real-time brain cancer detection, in order to assist neurosurgeons during tumour resection procedures. One of the main problems associated to brain tumours is its infiltrative nature, which makes complete tumour resection a highly difficult task. With the combination of Hyperspectral Imaging and Machine Learning techniques, the project aimed at demonstrating that a precise determination of tumour boundaries was possible, helping this way neurosurgeons to minimize the amount of removed healthy tissue. The project partners involved, besides different universities and companies, two hospitals where the demonstrator was tested during surgical procedures. This paper introduces the difficulties around brain tumor resection, stating the main objectives of the project and presenting the materials, methodologies and platforms used to propose a solution. A brief summary of the main results obtained is also included. © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.
URI: http://hdl.handle.net/10553/44978
ISBN: 9781450344876
DOI: 10.1145/3075564.3076262
Source: ACM International Conference on Computing Frontiers 2017, CF 2017, p. 313-318
Appears in Collections:Actas de congresos
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