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Author
Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño
Title
Texture-based Polyp Detection in Colonoscopy
Type
Journal Article
Year
2009
Publication
Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN
Abbreviated Journal
Volume
Issue
Pages
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
800
Expedition
Conference
Notes
MV;SIAI
Approved
no
Call Number
fernando @ fernando @
Serial
2428
Permanent link to this record
Author
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville
Title
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Type
Journal Article
Year
2017
Publication
Journal of Healthcare Engineering
Abbreviated Journal
JHCE
Volume
Issue
Pages
2040-2295
Keywords
Colonoscopy images; Deep Learning; Semantic Segmentation
Abstract
Colorectal cancer (CRC) is the third cause of cancer death world-wide. 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 inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing 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 endolumninal scene, tar- geting 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 (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
Address
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Editor
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Summary Language
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Series Title
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Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118
Approved
no
Call Number
VBS2017b
Serial
2940
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