Detection of Villous Atrophy Using Endoscopic Images for the Diagnosis of Celiac Disease

Ciaccio, Edward J.; Lewis, Suzanne K.; Green, Peter H.

doi:10.1007/s10620-013-2618-9

Cited by 6 publications

(2 citation statements)

References 31 publications

(32 reference statements)

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“…It is difficult to detect villous atrophy in part because the spatial resolution of the video capsule system from which discretized images are obtained is limited, and does not in every case clearly detect the individual villi in the small intestinal wall. The resolution in part depends on the video camera to intestinal wall distance, the camera lighting, and the camera angle, and is at best about 1 mm[ 17 ]. All of the factors for which resolution is determined are variable and tend to be random.…”

Section: Methodsmentioning

confidence: 99%

Recommendations to quantify villous atrophy in video capsule endoscopy images of celiac disease patients

Ciaccio

Bhagat

Lewis

et al. 2016

WJGE

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AIMTo quantify the presence of villous atrophy in endoscopic images for improved automation.METHODSThere are two main categories of quantitative descriptors helpful to detect villous atrophy: (1) Statistical and (2) Syntactic. Statistical descriptors measure the small intestinal substrate in endoscope-acquired images based on mathematical methods. Texture is the most commonly used statistical descriptor to quantify villous atrophy. Syntactic descriptors comprise a syntax, or set of rules, for analyzing and parsing the substrate into a set of objects with boundaries. The syntax is designed to identify and distinguish three-dimensional structures based on their shape.RESULTSThe variance texture statistical descriptor is useful to describe the average variability in image gray level representing villous atrophy, but does not determine the range in variability and the spatial relationships between regions. Improved textural descriptors will incorporate these factors, so that areas with variability gradients and regions that are orientation dependent can be distinguished. The protrusion syntactic descriptor is useful to detect three-dimensional architectural components, but is limited to identifying objects of a certain shape. Improvement in this descriptor will require incorporating flexibility to the prototypical template, so that protrusions of any shape can be detected, measured, and distinguished.CONCLUSIONImproved quantitative descriptors of villous atrophy are being developed, which will be useful in detecting subtle, varying patterns of villous atrophy in the small intestinal mucosa of suspected and known celiac disease patients.

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Section: Methodsmentioning

confidence: 99%

Recommendations to quantify villous atrophy in video capsule endoscopy images of celiac disease patients

Ciaccio

Bhagat

Lewis

et al. 2016

WJGE

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“…3 The gold standard in the diagnosis of CD is the demonstration of VA in duodenal biopsies. 4 VA is investigated extensively in the medical community, [5][6][7] and image processing methods as well as quantitative computational methods are highly needed, required and recommended from the community for the characterization of the small intestinal mucosa in suspected and known CD patients. 8 At variance with zoom endoscopy, confocal laser endomicroscopy (CLE) is a relatively novel endoscopic method that permits on-site microscopy of the gastrointestinal mucosa after the application of a fluorescent agent, allowing the experienced endoscopists to diagnose VA, CH as well as intraepithelial lymphocytes with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

et al. 2016

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Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Nave-Bayes quadratic approach and a standard quadratic analysis, all validated with a tenfold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Nave Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

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Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach

Koh

Michele

Sudarshan

et al. 2021

Computer Methods and Programs in Biomedicine

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Detection of Villous Atrophy Using Endoscopic Images for the Diagnosis of Celiac Disease

Cited by 6 publications

References 31 publications

Recommendations to quantify villous atrophy in video capsule endoscopy images of celiac disease patients

Recommendations to quantify villous atrophy in video capsule endoscopy images of celiac disease patients

Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach

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