InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration
This work addresses the need for efficient and scalable template generation in medical imaging, particularly for brain MRI analysis, though it is incremental as it builds on existing VAE approaches.
The paper tackles the problem of slow and scale-limited groupwise template generation for brain MRI registration by proposing InstantGroup, a VAE-based framework that reduces runtime to seconds for any group size while achieving superior unbiasedness and registration accuracy compared to state-of-the-art methods.
Template generation is a critical step in groupwise image registration, which involves aligning a group of subjects into a common space. While existing methods can generate high-quality template images, they often incur substantial time costs or are limited by fixed group scales. In this paper, we present InstantGroup, an efficient groupwise template generation framework based on variational autoencoder (VAE) models that leverage latent representations' arithmetic properties, enabling scalability to groups of any size. InstantGroup features a Dual VAE backbone with shared-weight twin networks to handle pairs of inputs and incorporates a Displacement Inversion Module (DIM) to maintain template unbiasedness and a Subject-Template Alignment Module (STAM) to improve template quality and registration accuracy. Experiments on 3D brain MRI scans from the OASIS and ADNI datasets reveal that InstantGroup dramatically reduces runtime, generating templates within seconds for various group sizes while maintaining superior performance compared to state-of-the-art baselines on quantitative metrics, including unbiasedness and registration accuracy.