Synthetic Augmentation for Anatomical Landmark Localization using DDPMs

TL;DR

This paper investigates using denoising diffusion probabilistic models (DDPMs) to generate synthetic medical images and landmarks, aiming to improve anatomical landmark localization (ALL) by augmenting training data with high-quality synthetic images.

Contribution

It introduces a novel DDPM-based data augmentation method with a 2-channel input for medical images and landmarks, and proposes new quality assessment techniques for synthetic data in ALL tasks.

Findings

Enhanced training dataset with synthetic images improves ALL accuracy.

Proposed quality assessment methods effectively evaluate synthetic image plausibility.

DDPM augmentation outperforms traditional augmentation methods in landmark localization tasks.

Abstract

Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious and costly nature of medical data acquisition and annotation. While traditional data augmentation, variational autoencoders (VAEs), and generative adversarial networks (GANs) have already been used to synthetically expand medical datasets, diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. In this study, we explore the use of denoising diffusion probabilistic models (DDPMs) for generating medical images and their corresponding heatmaps of landmarks to enhance the training of a supervised deep learning model for ALL. Our novel approach involves a DDPM with a 2-channel input, incorporating both the original medical image and its heatmap of annotated landmarks. We also propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility, before we evaluate the DDPM-augmented dataset in the context of an ALL task involving hand X-Rays.