Fully convolutional neural networks for polyp segmentation in colonoscopy

Patrick Brandao; Evangelos Mazomenos; Gastone Ciuti; Renato Caliò; Federico Bianchi; Arianna Menciassi; Paolo Dario; Anastasios Koulaouzidis; Alberto Arezzo; Danail Stoyanov

doi:10.1117/12.2254361

3 March 2017 Fully convolutional neural networks for polyp segmentation in colonoscopy

Patrick Brandao, Evangelos Mazomenos, Gastone Ciuti, Renato Caliò, Federico Bianchi, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, Danail Stoyanov

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340F (2017) https://doi.org/10.1117/12.2254361
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Patrick Brandao, Evangelos Mazomenos, Gastone Ciuti, Renato Caliò, Federico Bianchi, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, and Danail Stoyanov "Fully convolutional neural networks for polyp segmentation in colonoscopy", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340F (3 March 2017); https://doi.org/10.1117/12.2254361

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available