Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a novel data augmentation framework for cardiac scar segmentation using implicit neural representations (INRs) and denoising diffusion models to synthesize late gadolinium enhancement (LGE) images and corresponding segmentation masks. INRs are trained to capture continuous spatial representations of LGE data and masks, compressed into latent embeddings, and then used by a diffusion model to generate new representations that are decoded into synthetic LGE images with anatomically consistent segmentation masks. Experiments demonstrate that augmenting training data with synthetic volumes improves fibrosis segmentation performance, increasing the Dice score from 0.509 to 0.524.
Forget manual annotation: this method generates synthetic cardiac MRI images with corresponding scar segmentations, boosting segmentation performance by leveraging implicit neural representations and diffusion models.
Late gadolinium enhancement (LGE) imaging is the clinical standard for myocardial scar assessment, but limited annotated datasets hinder the development of automated segmentation methods. We propose a novel framework that synthesises both LGE images and their corresponding segmentation masks using implicit neural representations (INRs) combined with denoising diffusion models. Our approach first trains INRs to capture continuous spatial representations of LGE data and associated myocardium and fibrosis masks. These INRs are then compressed into compact latent embeddings, preserving essential anatomical information. A diffusion model operates on this latent space to generate new representations, which are decoded into synthetic LGE images with anatomically consistent segmentation masks. Experiments on 133 cardiac MRI scans suggest that augmenting training data with 200 synthetic volumes contributes to improved fibrosis segmentation performance, with the Dice score showing an increase from 0.509 to 0.524. Our approach provides an annotation-free method to help mitigate data scarcity.The code for this research is publicly available.