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.

翻译：晚期钆增强（LGE）成像是评估心肌瘢痕的临床金标准，但有限的标注数据集阻碍了自动化分割方法的发展。我们提出了一种新颖的框架，该框架结合隐式神经表征（INRs）与去噪扩散模型，同步合成LGE图像及其对应的分割掩码。我们的方法首先训练INRs以捕获LGE数据及其相关的心肌与纤维化掩码的连续空间表征。随后，这些INRs被压缩为紧凑的潜在嵌入，以保留关键的解剖信息。一个扩散模型在此潜在空间中进行操作，生成新的表征，这些表征被解码为具有解剖结构一致的分割掩码的合成LGE图像。在133例心脏MRI扫描数据上的实验表明，使用200个合成体积数据增强训练集有助于提升纤维化分割性能，Dice分数从0.509提高至0.524。我们的方法提供了一种无需标注的途径，有助于缓解数据稀缺问题。本研究的代码已公开提供。