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Proceedings Paper

Fully convolutional neural networks for polyp segmentation in colonoscopy
Author(s): Patrick Brandao; Evangelos Mazomenos; Gastone Ciuti; Renato Caliò; Federico Bianchi; Arianna Menciassi; Paolo Dario; Anastasios Koulaouzidis; Alberto Arezzo; Danail Stoyanov
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Paper Abstract

Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340F (3 March 2017); doi: 10.1117/12.2254361
Show Author Affiliations
Patrick Brandao, Univ. College London (United Kingdom)
Evangelos Mazomenos, Univ. College London (United Kingdom)
Gastone Ciuti, The BioRobotics Institute, Scuola Superiore Sant'Anna (Italy)
Renato Caliò, The BioRobotics Institute, Scuola Superiore Sant'Anna (Italy)
Federico Bianchi, The BioRobotics Institute, Scuola Superiore Sant'Anna (Italy)
Arianna Menciassi, The BioRobotics Institute, Scuola Superiore Sant'Anna (Italy)
Paolo Dario, The BioRobotics Institute, Scuola Superiore Sant'Anna (Italy)
Anastasios Koulaouzidis, The Royal Infirmary of Edinburgh (United Kingdom)
Alberto Arezzo, Univ. degli Studi di Torino (Italy)
Danail Stoyanov, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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