WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Open accessAbstract
We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WM-DOVA (Window Median Depth of Valleys Accumulation) energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice.
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Cites
Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008
Ferlay, J., Shin, H.-R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010) · International Journal of Cancer · doi:10.1002/ijc.25516 · Cited by 22,207 · see citing works
Prevention of Colorectal Cancer by Colonoscopic Polypectomy
Winawer, S. J., Zauber, A. G., Ho, M. N., O’Brien, M. J., Gottlieb, L. S., Sternberg, S. S., Waye, J. D., Schapiro, M., Bond, J. H., Panish, J. F., et al. (1993) · New England Journal of Medicine · doi:10.1056/nejm199312303292701 · Cited by 4,585 · see citing works
Harel, J., Koch, C., & Perona, P. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,494 · see citing works
A saliency-based search mechanism for overt and covert shifts of visual attention
Itti, L., & Koch, C. (2000) · Vision Research · doi:10.1016/s0042-6989(99)00163-7 · Cited by 3,137 · see citing works
Torralba, A. B., Oliva, A., Castelhano, M. S., & Henderson, J. M. (2006) · Psychological Review · doi:10.1037/0033-295x.113.4.766 · Cited by 1,646 · see citing works
GLOBOCAN 2008 v1.2, Cancer incidence and mortality world-wide : IARC Cancer Base No. 10
Ferlay, J. (2010) · Medical Entomology and Zoology · Cited by 1,636 · see citing works
Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review
van Rijn, J. C., Reitsma, J. B., Stoker, J., Bossuyt, P. M., van Deventer, S. J. H., & Dekker, E. (2006) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2006.00390.x · Cited by 1,343 · see citing works
SUN: A Bayesian framework for saliency using natural statistics
Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008) · Journal of Vision · doi:10.1167/8.7.32 · Cited by 1,228 · see citing works
Inoue, H., Kashida, H., Kudo, S., Sasako, M., Shimoda, T., Watanabe, H., Yoshida, S., Guelrud, M., Cj, L., Wang, K., et al. (2003) · IRIS · Cited by 1,149 · see citing works
Saliency Based on Information Maximization
Bruce, N. D. B., & Tsotsos, J. K. (2005) · neural information processing systems · Cited by 1,094 · see citing works
Mendonça, A. M., & Campilho, A. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879955 · Cited by 927 · see citing works
Colonoscopy versus Fecal Immunochemical Testing in Colorectal-Cancer Screening
Quintero, E., Castells, A., Bujanda, L., Cubiella, J., Salas, D., Lanas, Á., Andreu, M. G., Carballo, F., Morillas, J. D., Hernández, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1108895 · Cited by 914 · see citing works
Towards automatic polyp detection with a polyp appearance model
Bernal, J., Sánchez, F. J., & Vilariño, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.03.002 · Cited by 709 · see citing works
Self-citationStatic and space-time visual saliency detection by self-resemblance
Seo, H. J., & Milanfar, P. (2009) · Journal of Vision · doi:10.1167/9.12.15 · Cited by 642 · see citing works
Müller, A., & Sonnenberg, A. (1995) · Annals of Internal Medicine · doi:10.7326/0003-4819-123-12-199512150-00002 · Cited by 605 · see citing works
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study
Borji, A., Sihite, D. N., & Itti, L. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2210727 · Cited by 602 · see citing works
Computer-aided tumor detection in endoscopic video using color wavelet features
Karkanis, S. A., Iakovidis, D. K., Maroulis, D., Karras, D. A., & Tzivras, M. (2003) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2003.813794 · Cited by 511 · see citing works
Risk of Developing Colorectal Cancer Following a Negative Colonoscopy Examination
Singh, H., Turner, D., Xue, L., Targownik, L. E., & Bernstein, C. N. (2006) · JAMA · doi:10.1001/jama.295.20.2366 · Cited by 427 · see citing works
Separating reflection components of textured surfaces using a single image
Tan, R. T., & Ikeuchi, K. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.36 · Cited by 386 · see citing works
Factors influencing the miss rate of polyps in a back-to-back colonoscopy study
Leufkens, A., van Oijen, M., Vleggaar, F., & Siersema, P. (2012) · Endoscopy · doi:10.1055/s-0031-1291666 · Cited by 375 · see citing works
Theodorsson-Norheim, E. (1986) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(86)90081-7 · Cited by 358 · see citing works
Saliency estimation using a non-parametric low-level vision model
Murray, N., Vanrell, M., Otazu, X., et al. (2011) · doi:10.1109/cvpr.2011.5995506 · Cited by 351 · see citing works
Gralnek, I. M., Siersema, P. D., Halpern, Z., et al. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(14)70020-8 · Cited by 238 · see citing works
Sample Sizes for Clinical Trials
Julious, S. A. (2009) · Chapman & Hall/CRC eBooks · doi:10.1201/9781584887409 · Cited by 227 · see citing works
Evaluation of methods for ridge and valley detection
López, A. M., Lumbreras, F., Serrat, J., et al. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.761263 · Cited by 213 · see citing works
Discriminant Saliency for Visual Recognition from Cluttered Scenes
Gao, D., & Vasconcelos, N. M. (2004) · Cited by 196 · see citing works
MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy
Coimbra, M. T., & Cunha, J. P. S. (2006) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2006.873158 · Cited by 185 · see citing works
Real-Time Specular Highlight Removal Using Bilateral Filtering
Yang, Q., Wang, S., & Ahuja, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_7 · Cited by 183 · see citing works
Polyp Detection in Colonoscopy Video using Elliptical Shape Feature
Hwang, S., Oh, J., Tavanapong, W., et al. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379193 · Cited by 167 · see citing works
Texture-Based Polyp Detection in Colonoscopy
Ameling, S., Wirth, S., Paulus, D. W. R., et al. (2009) · Informatik aktuell · doi:10.1007/978-3-540-93860-6_70 · Cited by 143 · see citing works
Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging
Arnold, M., Ghosh, A., Ameling, S., et al. (2010) · EURASIP Journal on Image and Video Processing · doi:10.1155/2010/814319 · Cited by 121 · see citing works
Khan, R. S. A., Tien, G., Atkins, M. S., et al. (2012) · Surgical Endoscopy · doi:10.1007/s00464-012-2400-7 · Cited by 113 · see citing works
USING A COLOR REFLECTION MODEL TO SEPARATE HIGHLIGHTS FROM OBJECT COLOR
Klinker, G. J., Shafer, S. A., Kanade, T., et al. (1987) · Cited by 105 · see citing works
Separating reflection components of textured surfaces using a single image
Tan, & Ikeuchi (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238440 · Cited by 76 · see citing works
Saint-Pierre, C.-A., Boisvert, J., Grimard, G., et al. (2007) · Machine Vision and Applications · doi:10.1007/s00138-007-0099-6 · Cited by 68 · see citing works
Intestinal abnormality detection from endoscopic images
Krishnan, S. M., Yang, X., Chan, K. L., et al. (2002) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.1998.745583 · Cited by 66 · see citing works
Impact of image preprocessing methods on polyp localization in colonoscopy frames
Bernal, J., Sanchez, J., & Vilariño, F. (2013) · doi:10.1109/embc.2013.6611256 · Cited by 54 · see citing works
Self-citationBretagne, J., Manfrédi, S., Piette, C., et al. (2010) · Diseases of the Colon & Rectum · doi:10.1007/dcr.0b013e3181c37f9c · Cited by 51 · see citing works
Real-time image processing system for endoscopic applications
Kang, J., & Doraiswami, R. (2004) · doi:10.1109/ccece.2003.1226181 · Cited by 47 · see citing works
Learning-Based Prediction of Visual Attention for Video Signals
Lee, W.-F., Huang, T., Yeh, S., et al. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2144610 · Cited by 37 · see citing works
Systematic review of the effectiveness of population screening for colorectal cancer.
Kerr, J., Day, P. J., Broadstock, M., Weir, R., & Bidwell, S. (2007) · PubMed · Cited by 29 · see citing works
Automatic Polyp Detection in Endoscope Images Using a Hessian Filter
Iwahori, Y., Shinohara, T., Hattori, A., et al. (2013) · Machine Vision and Applications · Cited by 25 · see citing works
Barton, R. E. (2008) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2008.03.022 · Cited by 13 · see citing works
Bernal, J., Sánchez, F. J., Vilariño, F., et al. (2014) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/2578153.2578189 · Cited by 11 · see citing works
Self-citationBildverarbeitung für die Medizin 2008
Brauer, W., Deserno, T. M., Braun, J., et al. (2008) · Informatik aktuell · doi:10.1007/978-3-540-78640-5 · Cited by 8 · see citing works
Towards Intelligent Systems for Colonoscopy
Bernal, J., Vilariño, F., & Sanchez, J. (2011) · InTech eBooks · doi:10.5772/19748 · Cited by 8 · see citing works
Self-citationIntegration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy
Bernal, J., Sánchez, J., & Vilariño, F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28557-8_10 · Cited by 7 · see citing works
Self-citationDepth of Valleys Accumulation Algorithm for Object Detection
Jorge, B., Javier, S., & Fernando, V. (2011) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-842-7-71 · Cited by 5 · see citing works
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Research works printed but not linked: 1
- C.M. Mann and H.J. Godwin et al., "Rapidly imparting the skills of experts to novice participants in threat assessment tasks", Book of Abstracts of ECEM 2013, vol. 6, no. 3, p. 245, 2013.
Cited by
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Towards a guideline for evaluation metrics in medical image segmentation
Müller, D., Soto-Rey, I., & Kramer, F. (2022) · BMC Research Notes · doi:10.1186/s13104-022-06096-y · Cited by 668 · see citing works
Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536 · see citing works
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Wickstrøm, K., Kampffmeyer, M., & Jenssen, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101619 · Cited by 185 · see citing works
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104815 · Cited by 186 · see citing works
Artificial intelligence and colonoscopy: Current status and future perspectives
Kudo, S., Mori, Y., Misawa, M., Takeda, K., Kudo, T., Itoh, H., Oda, M., & Mori, K. (2019) · Digestive Endoscopy · doi:10.1111/den.13340 · Cited by 147 · see citing works
Volumetric memory network for interactive medical image segmentation
Zhou, T., Li, L., Bredell, G., Li, J., Unkelbach, J., & Konukoglu, E. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102599 · Cited by 109 · see citing works
Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation
Ma, Y., Wang, J., Yang, J., & Wang, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3348982 · Cited by 55 · see citing works
Zhang, Y., Pan, Y., Zhong, T., Dong, P., Xie, K., Liu, Y., Jiang, H., Wu, Z., Liu, Z., Zhao, W., et al. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100103 · Cited by 52 · see citing works
Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation
Gupta, S., & Singla, J. (2026) · arXiv · doi:10.48550/arxiv.2609.10495
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- Authors
- Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- Hospital Clínic de Barcelona (ES)
- Corresponding author
- Bernal, Jorge
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 43
- Pages
- 99-111
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2008359794
- Cited by
- 2,191
- References
- 48
- Citations by year
- 2026: 451 · 2025: 524 · 2024: 434 · 2023: 305 · 2022: 194 · 2021: 125 · 2020: 69 · 2019: 38 · 2018: 27 · 2017: 13 · 2016: 7 · 2015: 3
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- green
- ddd.uab.cat
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by-nc-nd)
- Funders
- Spanish Government
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- polyp localization, colonoscopy, saliency maps, eye tracking, polyp detection, small polyps, polyp miss rate, energy map
- MeSH terms
- Colonic Polyps, Colonoscopy, Humans, Image Processing, Computer-Assisted, Sensitivity and Specificity, Colorectal Neoplasms
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