The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Open accessAbstract
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
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The pascal visual object classes (VOC) challenge
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Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Self-citationBernal, 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
Self-citationToward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer
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Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
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PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59725-2_26 · Cited by 1,499
TransFuse: 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
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,296
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
Medical Image Segmentation via Cascaded Attention Decoding
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Metadata
- Authors
- Vázquez, D., Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., López, A. M., Romero, A., Drozdzal, M., & Courville, A.
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-9
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- OpenAlex
- W2560328367
- Cited by
- 866
- References
- 19
- FWCI
- 5.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Vázquez, D., Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., López, A. M., Romero, A., Drozdzal, M., & Courville, A. (2017). A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. Journal of Healthcare Engineering. https://doi.org/10.1155/2017/4037190
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