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Open AccessArticle

Pre-Cancerous Stomach Lesion Detections with Multispectral-Augmented Endoscopic Prototype

ImViA EA 7535, Univ. Bourgogne Franche-Comté, 21078 Dijon, France
Hôpital Ambroise Paré de Boulogne Billancourt, 92100 Boulogne-Billancourt, France
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(3), 795;
Received: 18 December 2019 / Revised: 14 January 2020 / Accepted: 19 January 2020 / Published: 22 January 2020
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
In this paper, we are interested in the in vivo detection of pre-cancerous stomach lesions. Pre-cancerous lesions are unfortunately rarely explored in research papers as most of them are focused on cancer detection or conducted ex-vivo. For this purpose, a novel prototype is introduced. It consists of a standard endoscope with multispectral cameras, an optical setup, a fiberscope, and an external light source. Reflectance spectra are acquired in vivo on 16 patients with a healthy stomach, chronic gastritis, or intestinal metaplasia. A specific pipeline has been designed for the classification of spectra between healthy mucosa and different pathologies. The pipeline includes a wavelength clustering algorithm, spectral features computation, and the training of a classifier in a “leave one patient out” manner. Good classification results, around 80%, have been obtained, and two attractive wavelength ranges were found in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm]. The new prototype and the associated results give good arguments in favor of future common use in operating rooms, during upper gastrointestinal exploration of the stomach for the detection of stomach diseases. View Full-Text
Keywords: multispectral imagin; stomach lesions; gastroendoscopy multispectral imagin; stomach lesions; gastroendoscopy
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Krebs, A.; Benezeth, Y.; Bazin, T.; Marzani, F.; Lamarque, D. Pre-Cancerous Stomach Lesion Detections with Multispectral-Augmented Endoscopic Prototype. Appl. Sci. 2020, 10, 795.

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