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A. Martinez; Jordi Vitria |
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Designing and Implementing Real Walking Agents using Virtual Environments. |
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1995 |
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Madeira, Portugal |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1995a |
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121 |
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Petia Radeva; Jordi Vitria |
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Corkinspect: Statistical Learning of Natural Material |
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2004 |
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Italian Beverage Technology, 13(38):11–18 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2004b |
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514 |
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Author |
Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva |
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CORKIDENT: an automatic vision system for real-time inspection of natural products. |
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1999 |
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Wales |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RMT1999 |
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23 |
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Cristina Sanchez Montes; F. Javier Sanchez; Jorge Bernal; Henry Cordova; Maria Lopez Ceron; Miriam Cuatrecasas; Cristina Rodriguez de Miguel; Ana Garcia Rodriguez; Rodrigo Garces Duran; Maria Pellise; Josep Llach; Gloria Fernandez Esparrach |
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Title |
Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis |
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2019 |
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Endoscopy |
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END |
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51 |
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3 |
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261-265 |
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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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MV; 600.096; 600.119; 600.075 |
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Admin @ si @ SSB2019 |
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3164 |
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Jorge Bernal; Nima Tajkbaksh; F. Javier Sanchez; Bogdan J. Matuszewski; Hao Chen; Lequan Yu; Quentin Angermann; Olivier Romain; Bjorn Rustad; Ilangko Balasingham; Konstantin Pogorelov; Sungbin Choi; Quentin Debard; Lena Maier Hein; Stefanie Speidel; Danail Stoyanov; Patrick Brandao; Henry Cordova; Cristina Sanchez Montes; Suryakanth R. Gurudu; Gloria Fernandez Esparrach; Xavier Dray; Jianming Liang; Aymeric Histace |
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Title |
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge |
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2017 |
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IEEE Transactions on Medical Imaging |
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TMI |
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36 |
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6 |
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1231 - 1249 |
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Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework |
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Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance. |
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MV; 600.096; 600.075 |
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Admin @ si @ BTS2017 |
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2949 |
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