Identification of precancerous lesions by multispectral gastroendoscopy

Herrera, Sergio Ernesto Martinez; Benezeth, Yannick; Boffety, Matthieu; Émile, Jean-François; Marzani, Franck; Lamarque, Dominique; Goudail, François

doi:10.1007/s11760-015-0779-z

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…During gastroendoscopic examination, the endoscope was focused on the antrum. Then we used the multispectral acquisition system as described in 15 . A xenon lamp was used as the light source, since it provides constant stable illumination, which can be easily filtered in order to provide illumination at different wavelengths.…”

Section: Methodsmentioning

confidence: 99%

Multispectral imaging detects gastritis consistently in mouse model and in humans

Bazin

Martinez-Herrera

Jobart-Malfait

et al. 2020

Sci Rep

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Gastritis constitutes the initial step of the gastric carcinogenesis process. Gastritis diagnosis is based on histological examination of biopsies. Non-invasive real-time methods to detect mucosal inflammation are needed. Tissue optical properties modify reemitted light, i.e. the proportion of light that is emitted by a tissue after stimulation by a light flux. Analysis of light reemitted by gastric tissue could predict the inflammatory state. The aim of our study was to investigate a potential association between reemitted light and gastric tissue inflammation. We used two models and three multispectral analysis methods available on the marketplace. We used a mouse model of Helicobacter pylori infection and included patients undergoing gastric endoscopy. In mice, the reemitted light was measured using a spectrometer and a multispectral camera. We also exposed patient’s gastric mucosa to specific wavelengths and analyzed reemitted light. In both mouse model and humans, modifications of reemitted light were observed around 560 nm, 600 nm and 640 nm, associated with the presence of gastritis lesions. These results pave the way for the development of improved endoscopes in order to detect real-time gastritis without the need of biopsies. This would allow a better prevention of gastric cancer alongside with cost efficient endoscopies.

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Section: Methodsmentioning

confidence: 99%

Multispectral imaging detects gastritis consistently in mouse model and in humans

Bazin

Martinez-Herrera

Jobart-Malfait

et al. 2020

Sci Rep

Self Cite

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“…Some papers have examined the scheme for feature selection. Sainju et al [17] and Sergio et al [18] adopted the wrapper approach to determine the best combination of feature values. Yeh et al [19] evaluated the performance of three filter-based methods.…”

Section: Feature Selectionmentioning

confidence: 99%

Real-time identification of blood regions for hemostasis support in laparoscopic surgery

Okamoto

Ohnishi

Kawahira

et al. 2018

SIViP

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Laparoscopic surgery allows reduction in surgical incision size and leads to faster recovery compared with open surgery. When bleeding takes place, hemostasis treatment is planned according to the state and location of the bleeding. However, it is difficult to find the bleeding source due to low visibility caused by the narrow field of view of the laparoscope. In this paper, we propose the concept of a hemostasis support system that automatically identifies blood regions and indicates them to the surgeon. We mainly describe a blood region identification method that is one of technical challenges to realize the support system. The proposed method is based on a machine learning technique called the support vector machine, working in real time. Within this method, all the pixels in the image are classified as either blood or non-blood pixels based on color features (e.g., a combination of RGB and HSV values). The suitable combination of feature values used for the classification is determined by a simple feature selection method. Three feature values were determined to identify the blood region. We then validated the proposed method with ten sequences of laparoscopic images by cross-validation. The average accuracy exceeded 95% with a processing time of about 12.6 ms/frame. The proposed method was able to accurately identify blood regions and was suitable for real-time applications.

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Spatial and Spectral Calibration of a Multispectral-Augmented Endoscopic Prototype

Zenteno¹,

Krebs²,

Treuillet³

et al. 2019

Communications in Computer and Information Science

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Identification of precancerous lesions by multispectral gastroendoscopy

Abstract: International audienc

Cited by 10 publications

References 29 publications

Multispectral imaging detects gastritis consistently in mouse model and in humans

Multispectral imaging detects gastritis consistently in mouse model and in humans

Real-time identification of blood regions for hemostasis support in laparoscopic surgery

Spatial and Spectral Calibration of a Multispectral-Augmented Endoscopic Prototype

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