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Authors: Willem Dijkstra 1 ; André Sobiecki 2 ; Jorge Bernal 3 and Alexandru C. Telea 4

Affiliations: 1 Bernoulli Institute, University of Groningen and The Netherlands ; 2 Bernoulli Institute, University of Groningen, The Netherlands, ZiuZ Visual Intelligence, Gorredijk and The Netherlands ; 3 Image Sequence Evaluation laboratory, Computer Vision Center and Universitat Autónoma de Barcelona and Spain ; 4 ZiuZ Visual Intelligence, Gorredijk and The Netherlands

Keyword(s): Machine Learning, CNNs, Polyp Detection, Polyp Segmentation, Colonoscopy.

Abstract: Colorectal cancer is one of the main causes of cancer death worldwide. Early detection of its precursor lesion, the polyp, is key to ensure patient survival. Despite its gold standard status, colonoscopy presents some drawbacks such as polyp misses. While several computer-based solutions in this direction have been proposed, there is no available solution tackling lesion detection, localization and segmentation at once. We present in this paper a one-shot solution to characterize polyps in colonoscopy images. Our method uses a fully convolutional neural network model for semantic segmentation. Next, we apply transfer learning to provide detection and localization. We tested our method on several public datasets showing promising results, including compliance with technical and clinical requirements needed for an efficient deployment in the exploration room.

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Paper citation in several formats:
Dijkstra, W.; Sobiecki, A.; Bernal, J. and Telea, A. (2019). Towards a Single Solution for Polyp Detection, Localization and Segmentation in Colonoscopy Images. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 616-625. DOI: 10.5220/0007694906160625

@conference{giana19,
author={Willem Dijkstra. and André Sobiecki. and Jorge Bernal. and Alexandru C. Telea.},
title={Towards a Single Solution for Polyp Detection, Localization and Segmentation in Colonoscopy Images},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA},
year={2019},
pages={616-625},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007694906160625},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: GIANA
TI - Towards a Single Solution for Polyp Detection, Localization and Segmentation in Colonoscopy Images
SN - 978-989-758-354-4
IS - 2184-4321
AU - Dijkstra, W.
AU - Sobiecki, A.
AU - Bernal, J.
AU - Telea, A.
PY - 2019
SP - 616
EP - 625
DO - 10.5220/0007694906160625
PB - SciTePress

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