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Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation
Abstract:
This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Fréchet Inception Distance (FID) score of 78.47, compared to scores above 95.82) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6828 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.
Insights
Polyp-DDPM generates realistic polyp images using diffusion models, improving gastrointestinal polyp segmentation. This method enhances data augmentation and segmentation model performance, addressing medical imaging data challenges.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Gastrointestinal (GI) polyp segmentation faces challenges due to limited medical data, high annotation costs, and privacy concerns.
- Existing methods struggle with generating diverse and high-quality synthetic data for training segmentation models.
Purpose of the Study:
- To introduce Polyp-DDPM, a novel diffusion-based model for generating realistic polyp images conditioned on segmentation masks.
- To enhance the performance of polyp segmentation models through improved data augmentation.
Main Methods:
- Utilized a diffusion model conditioned on binary segmentation masks to generate synthetic polyp images.
- Evaluated image quality using Fréchet Inception Distance (FID) and segmentation performance using Intersection over Union (IoU).
Main Results:
- Polyp-DDPM achieved a superior FID score of 78.47, outperforming baseline models.
- The method resulted in an IoU of 0.7156 for polyp segmentation, surpassing baseline synthetic data and approaching real data performance.
- Generated synthetic data comparable in quality and segmentation enhancement capabilities to real medical images.
Conclusions:
- Polyp-DDPM effectively generates high-quality, diverse synthetic polyp images, addressing data limitations in medical imaging.
- The approach significantly enhances polyp segmentation model performance, offering a valuable tool for medical data augmentation.
- Publicly available code and weights facilitate further research and application in GI tract polyp analysis.
