标题： 胎儿和新生儿病理性MRI分割的合成病理性数据生成 显示英文标题

标题： Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates

摘要： 开发用于临床胎儿和新生儿MRI数据自动分析的新方法受到标注病理数据集稀缺性和隐私问题的限制，这些因素通常会限制数据共享，从而影响深度学习模型的有效性。 我们通过两种方式来解决这个问题。 首先，我们引入了Fetal&Neonatal-DDPM，这是一种新的扩散模型框架，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，我们通过形态学改变来修改健康标签图像，以模拟如脑室扩大、小脑和桥小脑发育不全以及小头畸形等情况，从而增强训练数据。 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现实的病理MRI。 放射科医生将合成的MRI评为在质量和诊断价值方面明显（p < 0.05）优于真实的MRI，表现出如血管和脉络丛等特征，并且与标签注释对齐度更好。 合成的病理数据提高了最先进的nnUNet分割性能，特别是在严重的脑室扩大病例中，改善最大的是脑室分割（Dice分数：0.9253 vs. 0.7317）。 本研究强调了生成式AI作为数据增强的变革性工具的潜力，在病理病例中提供了改进的分割性能。 这一发展代表了在改善产前成像分析和分割准确性方面的重要一步，并通过生成病理图像数据提供了数据匿名化的新方法。 显示英文摘要

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.