PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

TL;DR

PolypConnect is a novel image inpainting pipeline that generates synthetic polyp images from non-polyp GI images, enhancing training datasets for better polyp detection in medical imaging.

Contribution

We introduce PolypConnect, a pipeline for converting non-polyp images into synthetic polyp images to augment training data for CAD systems in gastrointestinal endoscopy.

Findings

Synthetic data improved segmentation accuracy by 5.1% mIOU.

Qualitative evaluations confirmed realism of generated images.

Endoscopists validated the synthetic images.

Abstract

Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.