用神经隐式表示生成心肌疤痕图像与分割图，解决标注数据少问题。
Synthesis of Late Gadolinium Enhancement Images via Implicit Neural Representations for Cardiac Scar Segmentation
- 用隐式神经表示学习真实LGE图像的连续空间特征。
- 合成200个新样本后，纤维化分割Dice分数提升至0.524。
- 无需人工标注，适合缺乏标注数据的医学图像研究者。
晚期钆增强（LGE）成像是评估心肌疤痕的临床标准，但标注数据集有限制约了自动化分割方法的发展。本文提出一种新框架，利用隐式神经表示（INRs）结合去噪扩散模型，合成LGE图像及其对应分割掩码。首先训练INRs以捕捉LGE数据及心肌、纤维化掩码的连续空间表征，再将这些INRs压缩为紧凑的潜在嵌入，保留关键解剖信息；随后在潜在空间中使用扩散模型生成新表征，并解码为具有解剖一致性分割掩码的合成LGE图像。在133例心脏MRI扫描上的实验表明，用200个合成体积扩充训练数据后，纤维化分割的Dice分数从0.509提升至0.524。该方法提供了一种无标注的数据增强方案，有助于缓解数据稀缺问题。代码已公开。
原文摘要 · Abstract (English)
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.
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