Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77938
Title: Hyperspectral imaging for tissue classification in glioblastoma tumor patients: a deep spectral-spatial approach
Authors: Manni, Francesca
Cai, Chuchen
van der Sommen, Fons
Zinger, Svitlana
Shan, Caifeng
Edström, Erik
Elmi-Terander, Adrian
Fabelo Gómez, Himar Antonio 
Ortega Sarmiento, Samuel 
Marrero Callicó, Gustavo Iván 
de With, Peter H. N.
UNESCO Clasification: 3311 tecnología de la instrumentación
3314 Tecnología médica
Keywords: Hyperspectral imaging
Glioblastoma tumor
Tissue classification
Hyperspectral imaging
Issue Date: 2021
Publisher: The international society for optics and photonics (SPIE)
Conference: Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 
Abstract: Surgery is a crucial treatment for malignant brain tumors where gross total resection improves the prognosis. Tissue samples taken during surgery are either subject to a preliminary intraoperative histological analysis, or sent for a full pathological evaluation which can take days or weeks. Whereas a lengthy complete pathological analysis includes an array of techniques to be executed, a preliminary tissue analysis on frozen tissue is performed as quickly as possible (30-45 minutes on average) to provide fast feedback to the surgeon during the surgery. The surgeon uses the information to confirm that the resected tissue is indeed tumor and may, at least in theory, initiate repeated biopsies to help achieve gross total resection. However, due to the total turn-around time of the tissue inspection for repeated analyses, this approach may not be feasible during a single surgery. In this context, intraoperative image-guided techniques can improve the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the potential to extract combined spectral-spatial information. By exploiting HSI for human brain-tissue classification in 13 in-vivo hyperspectral images from 9 patients, a brain-tissue classifier is developed. The framework consists of a hybrid 3D-2D CNN-based approach and a band-selection step to enhance the capability of extracting both spectral and spatial information from the hyperspectral images. An overall accuracy of 77% was found when tumor, normal and hyper-vascularized tissue are classified, which clearly outperforms the state-of-the-art approaches (SVM, 2D-CNN). These results may open an attractive future perspective for intraoperative brain-tumor classification using HSI.
URI: http://hdl.handle.net/10553/77938
ISBN: 9781510640252
DOI: 10.1117/12.2580158
Source: Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159810 (15 February 2021)
Appears in Collections:Actas de congresos
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