Modeling the Neonatal Brain Development Using Implicit Neural Representations

• URL: http://arxiv.org/abs/2408.08647v1
• Date: Fri, 16 Aug 2024 10:22:54 GMT
• Title: Modeling the Neonatal Brain Development Using Implicit Neural Representations
• Authors: Florentin Bieder, Paul Friedrich, Hélène Corbaz, Alicia Durrer, Julia Wolleb, Philippe C. Cattin,
• Abstract summary: 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 show that our method can be applied in a memory-efficient way, which is especially important for 3D data.
• Score: 1.5550533143704954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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