Modeling the Neonatal Brain Development Using Implicit Neural Representations
This work addresses the problem of accurately modeling brain development in neonates for medical research, but it is incremental as it builds on existing INR techniques.
The paper tackles modeling neonatal brain development from MR images using implicit neural representations, achieving subject-specific disentanglement of age and identity with proposed methods SSL and SGLA, and demonstrating memory efficiency for 3D data.
The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the developing human connectome project (dHCP). We propose a neural network, specifically an implicit neural representation (INR), to predict 2D- and 3D images of varying time points. In order to model a subject-specific development process, it is necessary to disentangle the age from the subjects' identity in the latent space of the INR. We propose two methods, Subject Specific Latent Vectors (SSL) and Stochastic Global Latent Augmentation (SGLA), enabling this disentanglement. We perform an analysis of the results and compare our proposed model to an age-conditioned denoising diffusion model as a baseline. We also show that our method can be applied in a memory-efficient way, which is especially important for 3D data.