Simple U-net based synthetic polyp image generation: Polyp to negative and negative to polyp. (April 2022)

• Record Type: Journal Article
• Authors: Qadir, Hemin Ali; Balasingham, Ilangko; Shin, Younghak
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Colonoscopy -- Polyp detection -- Image synthesis -- Convolutional neural network -- Generative adversarial networks; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103491
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: This study propose a novel conditional GAN-based framework for generation of realistic synthetic colon polyps The developed framework converts a given polyp image to a negative image and then the generated negative image back to a new-looking synthetic polyp. For a given image, polyps with various features are generated by controlling the value of polyp mask in the input condition images. The experimental results verify performance improvement when the generated synthetic polyp images are used for training various deep leaning based polyp detection and segmentation models. Abstract: Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a negative image (image without a polyp) using a simple conditional GAN architecture and then converts the negative image into a new-looking polyp image using the same network. In addition, by using the controllable polyp masks, polyps with various characteristics can be generated from one input condition. The generated polyp images can be used directly as training images for polyp detection and segmentation without additional labeling. To quantitatively assess the quality of generated synthetic polyps, we use public polyp image and video datasets combined with the generated synthetic images to examine the performance improvement of several detection and segmentation models. Experimental results show that we obtain performance gains when the generated polyp images are added to the training set.