The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
Abstract
This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Siegel, R. L., Miller, K. D., & Jemal, A. (2015) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21254 · Cited by 11,853
Mcgill, R., Tukey, J. W., & Larsen, W. A. (1978) · The American Statistician · doi:10.1080/00031305.1978.10479236 · Cited by 2,490
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.02.007 · Cited by 2,191
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
Tola, E., Lepetit, V., & Fua, P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.77 · Cited by 1,405
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., & Dekker, E. (2006) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2006.00390.x · Cited by 1,344
Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer
Silva, J., Histace, A., Romain, O., Dray, X., & Granado, B. (2014) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0926-3 · Cited by 1,309
CT Colonography versus Colonoscopy for the Detection of Advanced Neoplasia
Kim, D. H., Pickhardt, P. J., Taylor, A. J., Leung, W. K., Winter, T. C., Hinshaw, J. L., Gopal, D. V., Reichelderfer, M., Hsu, R. H., & Pfau, P. R. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa070543 · Cited by 706
Towards automatic polyp detection with a polyp appearance model
Bernal, J., Sánchez, J., & Vilariño, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.03.002 · Cited by 705
Cited by
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Self-citationTransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Zhang, Y., Liu, H., & Hu, Q. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87193-2_2 · Cited by 1,312
Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317500 · Cited by 865
Medical Image Segmentation via Cascaded Attention Decoding
Rahman, M. M., & Marculescu, R. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00616 · Cited by 382
Automatic Polyp Segmentation via Multi-scale Subtraction Network
Zhao, X., Zhang, L., & Lu, H. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87193-2_12 · Cited by 319
Metadata
- Authors
- Tajbakhsh, N., Gurudu, S. R., & Liang, J.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-02
- Volume
- 35
- Issue
- 2
- Pages
- 630-644
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2285968993
- Cited by
- 1,203
- References
- 38
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- closed
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