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Gloria Fernandez Esparrach, Jorge Bernal, Maria Lopez Ceron, Henry Cordova, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, et al. (2016). Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. END - Endoscopy, 48(9), 837–842.
Abstract: Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
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Cristina Sanchez Montes, F. Javier Sanchez, Jorge Bernal, Henry Cordova, Maria Lopez Ceron, Miriam Cuatrecasas, et al. (2019). Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis. END - Endoscopy, 51(3), 261–265.
Abstract: Background and study aims: To evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images.
Patients and methods: Textural elements (textons) were characterized according to their contrast with respect to the surface, shape and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis by the endoscopists using Kudo and NICE classification.
Results: Images of 225 polyps were evaluated (142 dysplastic and 83 non-dysplastic). CAD system correctly classified 205 (91.1%) polyps, 131/142 (92.3%) dysplastic and 74/83 (89.2%) non-dysplastic. For the subgroup of 100 diminutive (<5 mm) polyps, CAD correctly classified 87 (87%) polyps, 43/50 (86%) dysplastic and 44/50 (88%) non-dysplastic. There were not statistically significant differences in polyp histology prediction based on CAD system and on endoscopist assessment.
Conclusion: A computer vision system based on the characterization of the polyp surface in the white light accurately predicts colorectal polyp histology.
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Jordi Vitria, & J. Llacer. (1996). Reconstructing 3D light microscopic images using the EM algorithm. Pattern Recognition Letters, 1491–1498.
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M. Bressan, & Jordi Vitria. (2003). Nonparametric Discriminant Analysis and Nearest Neighbor Classification. PRL - Pattern Recognition Letters, 24(15), 2743–2749.
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David Guillamet, Jordi Vitria, & B. Shiele. (2003). Introducing a weighted non-negative matrix factorization for image classification. PRL - Pattern Recognition Letters, 24(14), 2447–2454.
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