Examining the effect of synthetic data augmentation in polyp detection and segmentation

Purpose As with several medical image analysis tasks based on deep learning, gastrointestinal image analysis is plagued with data scarcity, privacy concerns and an insufficient number of pathology samples. This study examines the generation and utility of synthetic samples of colonoscopy images with polyps for data augmentation. Methods We modify and train a pix2pix model to generate synthetic colonoscopy samples with polyps to augment the original dataset. Subsequently, we create a variety of datasets by varying the quantity of synthetic samples and traditional augmentation samples, to train a U-Net network and Faster R-CNN model for segmentation and detection of polyps, respectively. We compare the performance of the models when trained with the resulting datasets in terms of F-1 score, intersection over union, precision and recall. Further, we compare the performances of the models with unseen polyp datasets to assess their generalization ability. Results The average F-1 coefficient and intersection over union are improved with increasing number of synthetic samples in U-Net over all test datasets. The performance of the Faster R-CNN model is also improved in terms of polyp detection, while decreasing the false-negative rate. Further, the experimental results for polyp detection outperform similar studies in the literature on the ETIS-PolypLaribDB dataset. Conclusion By varying the quantity of synthetic and traditional augmentation, there is the potential to control the sensitivity of deep learning models in polyp segmentation and detection. Further, GAN-based augmentation is a viable option for improving the performance of models for polyp segmentation and detection.