1. Identification of precancerous lesions by multispectral gastroendoscopy
- Author
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François Goudail, Jean-François Emile, Matthieu Boffety, Sergio Ernesto Martinez Herrera, Franck Marzani, Dominique Lamarque, Yannick Benezeth, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Laboratoire Charles Fabry / Spim, Laboratoire Charles Fabry (LCF), Université Paris-Sud - Paris 11 (UP11)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS), Service d'anatomie pathologique [CHU Ambroise-Paré], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Ambroise Paré [AP-HP], Biomarqueurs et essais cliniques en Cancérologie et Onco-Hématologie (BECCOH), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Saclay, Hôpital Ambroise Paré [AP-HP], Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Universite de Versailles Saint Quentin en Yvelines, Laboratoire épidémiologie et oncogénèse des tumeurs digestives, Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Charles Fabry ( LCF ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut d'Optique Graduate School ( IOGS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut d'Optique Graduate School ( IOGS ) -Centre National de la Recherche Scientifique ( CNRS ), Service d'anatomie pathologique, Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Assistance publique - Hôpitaux de Paris (AP-HP)-Hôpital Ambroise Paré, Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Hôpital Ambroise Paré, and Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Assistance publique - Hôpitaux de Paris (AP-HP)
- Subjects
Artificial neural network ,Computer science ,business.industry ,Multispectral image ,Vector quantization ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,02 engineering and technology ,[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,3. Good health ,Visualization ,Support vector machine ,03 medical and health sciences ,Identification (information) ,0302 clinical medicine ,Pooled variance ,030220 oncology & carcinogenesis ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,White light ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,ComputingMilieux_MISCELLANEOUS - Abstract
Gastric cancer is one of the fifth most deadly cancers worldwide. Nowadays the diagnosis is performed through gastroendoscopy under white light and histological analysis. However, the precancerous lesions are multifocalized and present low differences with respect to healthy tissue. Several systems have been proposed based on light tissue interaction to improve the visualization of malignancies. However, these systems are limited to few wavelengths. In this paper, we propose a minimally invasive technique based on multispectral imaging and a methodology to identify malignancies in the stomach. We developed a multispectral gastroendoscopic system compatible with current gastroendoscopes, where only the illumination is changed. The spectra are extracted from the acquired multispectral images in order to compute statistical features that are used to classify the data in two classes: healthy and malignant. The features are ranked by pooled variance t test to train three classifiers. Neural networks using generalized relevance learning vector quantization, support vector machine (SVM) with a Gaussian kernel and k-nn are evaluated using leave one patient out cross-validation. Taking into consideration the data collected in this work, the quantitative results from the classification using SVM show high accuracy and sensitivity using a low number of features. These results show the ability to discriminate malignancies of the gastric tissue. Therefore, multispectral imaging could help in the identification of malignancies during gastroendoscopy.
- Published
- 2016
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