Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.

翻译：生成建模与合成数据可替代真实医学影像数据集，因其稀缺性和共享困难常对医疗应用中的精确深度学习模型部署造成阻碍。近年来，利用生成对抗网络（GANs）或扩散模型（DMs）等架构进行数据增强和合成数据共享的研究兴趣日益增长。然而，由于生成的影像缺乏关联标签，合成数据在三维磁共振成像（MRI）分割等任务中的应用仍十分有限。此外，许多已提出的生成式MRI模型因缺乏显式对比度条件控制而无法生成任意模态。这些局限性阻碍了用户对图像对比度和内容进行调节，也难以获得更具泛化能力的数据来训练特定任务模型。本文提出brainSPADE3D——一种用于脑部MRI及其关联分割的三维生成模型，用户可对特定病理表型和对比度进行条件控制。该联合成像-分割生成模型能够生成高保真合成影像及其关联分割结果，并具备组合病理的能力。实验证明，该模型可缓解数据中存在意外病理时分割模型性能下降的问题。