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A benchmark for endoluminal scene segmentation of colonoscopy images

David Vazquez, Jorge Bernal, F. Javier Sánchez, Gloria Fernández-Esparrach, Antonio M. López, Adriana Romero, Michał Drożdżal and Aaron Courville

Article (2017)

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Abstract

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.

Subjects: 1900 Biomedical engineering > 1900 Biomedical engineering
2600 Robotics > 2603 Computer vision
2700 Information technology > 2700 Information technology
2800 Artificial intelligence > 2800 Artificial intelligence (Computer vision, see 2603)
Department: Department of Computer Engineering and Software Engineering
Funders: Imagia Inc., Spanish government, iVENDIS, SGR Projects, CERCA Programme/ Generalitat de Catalunya, TECNIOspring-FP7-ACCI grant, FSEED, NVIDIA Corporation
Grant number: AC/DC TRA2014-57088-C2-1-R, DPI2015-65286-R, 2014-SGR-1506, 2014-SGR-1470, 2014-SGR-135
PolyPublie URL: https://publications.polymtl.ca/3596/
Journal Title: Journal of Healthcare Engineering (vol. 2017)
Publisher: Hindawi Publishing Corporation
DOI: 10.1155/2017/4037190
Official URL: https://doi.org/10.1155/2017/4037190
Date Deposited: 09 Mar 2020 15:57
Last Modified: 30 Sep 2023 17:31
Cite in APA 7: Vazquez, D., Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., López, A. M., Romero, A., Drożdżal, M., & Courville, A. (2017). A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering, 2017, 1-9. https://doi.org/10.1155/2017/4037190

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