IDDF2018-ABS-0257 Detecting and segmenting polyps using a deep learning-based model. (June 2018)

• Record Type: Journal Article
• Authors: Wang, Liansheng; Wang, Shuxin; Hu, Yanxing; Huang, Shaohui
• Is Part Of: Gut. Volume 67(2018)Supplement 2
• Page Start: A82
• Page End: A83
• Publication Date: 2018-06
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2018-IDDFabstracts.178
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Background: Colorectal cancer is the third most common cancer in the world, which developed from untreated polyps. Detecting polyps in their early stage have become a serious medical issue. Wireless capsule endoscopy (WCE) was designed to examine the intestinal diseases without surgery and can give a direct visualisation of intestines, in which the time cost is to analyse lots of images to capture the abnormal parts. The doctors will be wearied with about 25 000 images per person produced by WCE. This study aims to build a computer-aided diagnosis system to help doctors analyse WCE images and detect polyps. Methods: We use a deep learning-based model to build a computer-aided diagnosis system of WCE images for the assignment of detecting and segmenting polyps. The U-Net network with both dices loss and cross entropy loss is employed in this work, which is considered as an encoding and decoding model. The data used were available from CVC-Colon and CVC-Clinic, it has 992 images with polyps totally. The data is shown in table 1. Comparison to other algorithms, the U-Net model can learn more features automatically and is a high-efficient method for our assignment. Results: The performance of polyp segmentation was evaluated by Dice's coefficient (also known as the Dice coefficient), which presents the degree of similarity between prediction and ground truth. The Dice's coefficient in our study is 84.15%. The segmentation results are shown in IDDF2018-ABS-0257 figure 1. Conclusions: WCE plays an essential role in diagnosis and prevention of colorectal cancer. However, distinguishing abnormal WCE images with polyps is a significant challenge because it needs high time cost. The results show that our method can help physicians analyse the WCE images and reduce their pressure. This method could be further utilised in the clinical trials to help physicians from the tedious image analysing work.