Method for Intestinal Polyp Segmentation by Improving DeepLabv3+ Network

Abstract

In order to enhance the detection rate of polyp of intestine under colonoscopy, an improved DeepLabv3+ network method for intestinal polyp segmentation is proposed. In the data preprocessing stage, using the nonlinear filtering characteristics of the median filter to remove the image reflection area, and Grab Cut algorithm is combined to pre-extract the polyp area. Coarse segmentation results of polyp location are obtained, which are superimposed with the original drawing to reinforce the signal strength of polyp location. In terms of network structure, this paper introduces the optimal dense prediction cell obtained through neural architecture search into DeepLabv3+ network, uses 3-layer depth separable convolution to gradually acquire segmentation results in the decoder part, so as to reduce incomplete segmentation in the segmentation process. In the experiment, through training and testing of CVC-ClinicDB data set, the average joining and merging ratio, Dice coefficient, sensitivity, precision and F1 value are used as judgment standard. The mean intersection over union reaches 0.947, and the other 4 indexes are all higher than 0.935. The experimental results show that compared with the existing methods, the proposed method in this paper improves the accuracy of intestinal polyp image segmentation to a certain extent, which can be used for reference in the processing and analysis of intestinal polyp images by deep learning.

Keywords

• improved deeplabv3+
• intestinal polyp
• neural architecture search
• incomplete segmentation