  Medical image analysis has become a prominent area where machine learning has
been applied. However, high quality, publicly available data is limited either
due to patient privacy laws or the time and cost required for experts to
annotate images. In this retrospective study, we designed and evaluated a
pipeline to generate synthetic labeled polyp images for augmenting medical
image segmentation models with the aim of reducing this data scarcity. In
particular, we trained diffusion models on the HyperKvasir dataset, comprising
1000 images of polyps in the human GI tract from 2008 to 2016. Qualitative
expert review, Fr\'echet Inception Distance (FID), and Multi-Scale Structural
Similarity (MS-SSIM) were tested for evaluation. Additionally, various
segmentation models were trained with the generated data and evaluated using
Dice score and Intersection over Union. We found that our pipeline produced
images more akin to real polyp images based on FID scores, and segmentation
performance also showed improvements over GAN methods when trained entirely, or
partially, with synthetic data, despite requiring less compute for training.
Moreover, the improvement persists when tested on different datasets,
showcasing the transferability of the generated images.
