Title
Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network
Abstract
Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we proposed a polyp segmentation method based on the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform effective post-processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512197
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords
Field
DocType
Biological Phenomena,Colonic Polyps,Colonoscopy,Colorectal Neoplasms,Humans,Neural Networks, Computer,Polyps
Colonoscopy,Pattern recognition,Convolutional neural network,Segmentation,Computer science,Artificial intelligence,Colorectal cancer
Conference
Volume
ISSN
ISBN
2018
1557-170X
978-1-5386-3647-3
Citations 
PageRank 
References 
5
0.45
11
Authors
7
Name
Order
Citations
PageRank
Mojtaba Akbari171.17
Majid Mohrekesh2102.91
E. Nasr-esfahani3234.23
S. M. R. Soroushmehr47121.08
Nader Karimi514532.75
Shadrokh Samavi623338.99
Kayvan Najarian726259.53