Deep Generative Model-Based Generation of Synthetic Individual-Specific Brain MRI Segmentations
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
To the best of our knowledge, all existing methods that can generate
synthetic brain magnetic resonance imaging (MRI) scans for a specific
individual require detailed structural or volumetric information about the
individual's brain. However, such brain information is often scarce, expensive,
and difficult to obtain. In this paper, we propose the first approach capable
of generating synthetic brain MRI segmentations -- specifically, 3D white
matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) segmentations --
for individuals using their easily obtainable and often readily available
demographic, interview, and cognitive test information. Our approach features a
novel deep generative model, CSegSynth, which outperforms existing prominent
generative models, including conditional variational autoencoder (C-VAE),
conditional generative adversarial network (C-GAN), and conditional latent
diffusion model (C-LDM). We demonstrate the high quality of our synthetic
segmentations through extensive evaluations. Also, in assessing the
effectiveness of the individual-specific generation, we achieve superior volume
prediction, with mean absolute errors of only 36.44mL, 29.20mL, and 35.51mL
between the ground-truth WM, GM, and CSF volumes of test individuals and those
volumes predicted based on generated individual-specific segmentations,
respectively.
