Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

Abstract

One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enables realistic polyp image generations while maintaining the original structures of the colonoscopy image frames. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid many contractions of a feature map size. An image resizing with convolution for upsampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively realistic but also help to improve polyp detection performance.

10 pages

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• Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?
• Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation
• Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp
• This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation
• Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy
• Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs
• Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis