IDDF2018-ABS-0259 Segmentation of intestinal polyps via a deep learning algorithm. (June 2018)

• Record Type: Journal Article
• Authors: Wang, Liansheng; Qian, Yuqi; Hu, Yanxing
• Is Part Of: Gut. Volume 67(2018)Supplement 2
• Page Start: A83
• Page End: A84
• Publication Date: 2018-06
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2018-IDDFabstracts.180
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Background: Colorectal is the third most commonly occurring cancer in men and the second most commonly occurring cancer in women. At least 80%–95% of colorectal cancers are evolved from intestinal polyps. Therefore early screening of intestinal polyps is one of the crucial ways to prevent colorectal cancer. WCE is widely used in the examination of the internal environment of the digestive tract. However, lots of medical images per patient undoubtedly create a considerable burden on doctors. Thus, in this paper, we describe a computer-assisted segmentation of intestinal polyps to help doctors visualise polyps in images. Methods: We choose DCNN (Deep Convolution Neural Network) as our model, which has shown outstanding performance in image segmentation tasks in recent years. The data we used to train the model including two open intestinal polyp datasets, CVC-ColonDB and CVC-ClinicDB. The data are divided into training data, validation data and test data. At the same time, we use cross-entropy as a model-optimised loss function and the IoU value as our model's evaluation criteria. Results: Table 1 shows that our model performed well on these three data. At the same time, it should be noted that our model performed significantly better on the training data than the test data as shown in IDDF2018-ABS-0259 Figure 1. This may be due to the lack of data and the variance between different patients. Conclusions: The experimental results show that our model has achieved good performance in the polyp segmentation task, which appears as an IoU value of more than 0.5 on the test set and visual examples illustrated in Figure 1. In addition, without the need for manual feature extraction and preprocessing, DCNN model shows good adaptation for polyps of different sizes.