通过合成病理数据生成实现胎儿与新生儿病理MRI分割
Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates
AI总结：
针对胎儿和新生儿病理MRI标注数据稀缺问题，提出基于扩散模型的Fetal&Neonatal-DDPM框架，通过对健康标签图像进行形态学改变生成合成病理MRI，显著提升了nnUNet在病理病例（如严重脑室扩大）上的分割性能。
摘要：
临床胎儿和新生儿MRI数据自动分析新方法的开发，受限于标注病理数据集的稀缺以及通常限制数据共享的隐私问题，这阻碍了深度学习模型的有效性。我们通过两种方式解决这一问题。首先，我们引入了Fetal&Neonatal-DDPM，这是一种新颖的扩散模型框架，旨在从语义标签图像生成高质量的合成病理胎儿和新生儿MRI。其次，我们通过对健康标签图像进行形态学改变来增强训练数据，以模拟脑室扩大、小脑和脑桥小脑发育不全以及小头症等病理状况。通过利用Fetal&Neonatal-DDPM，我们从这些修改后的病理标签图像中合成了逼真的病理MRI。放射科医生评估认为，与真实MRI相比，合成MRI在质量和诊断价值上显著（p < 0.05）更优，展示了血管和脉络丛等特征，并改善了与标签注释的对齐情况。合成病理数据增强了最先进的nnUNet分割性能，特别是对于严重的脑室扩大病例，在脑室分割方面取得了最大的改进（Dice分数：0.9253 vs. 0.7317）。本研究强调了生成式AI作为数据增强变革性工具的潜力，在病理病例中提供了更好的分割性能。这一发展代表了在提高产前成像分析和分割准确性方面迈出的重要一步，同时也通过生成病理图像数据为数据匿名化提供了新方法。
英文摘要：
Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datasets and privacy concerns that often restrict data sharing, hindering the effectiveness of deep learning models. We address this in two ways. First, we introduce Fetal&Neonatal-DDPM, a novel diffusion model framework designed to generate high-quality synthetic pathological fetal and neonatal MRIs from semantic label images. Second, we enhance training data by modifying healthy label images through morphological alterations to simulate conditions such as ventriculomegaly, cerebellar and pontocerebellar hypoplasia, and microcephaly. By leveraging Fetal&Neonatal-DDPM, we synthesize realistic pathological MRIs from these modified pathological label images. Radiologists rated the synthetic MRIs as significantly (p < 0.05) superior in quality and diagnostic value compared to real MRIs, demonstrating features such as blood vessels and choroid plexus, and improved alignment with label annotations. Synthetic pathological data enhanced state-of-the-art nnUNet segmentation performance, particularly for severe ventriculomegaly cases, with the greatest improvements achieved in ventricle segmentation (Dice scores: 0.9253 vs. 0.7317). This study underscores the potential of generative AI as transformative tool for data augmentation, offering improved segmentation performance in pathological cases. This development represents a significant step towards improving analysis and segmentation accuracy in prenatal imaging, and also offers new ways for data anonymization through the generation of pathologic image data.
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