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 VAEs 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.

翻译：模板生成是群体图像配准中的关键步骤，其目标是将一组受试者图像对齐至一个公共空间。尽管现有方法能够生成高质量的模板图像，但它们通常需要耗费大量时间成本，或受限于固定的群体规模。本文提出InstantGroup，一种基于变分自编码器（VAE）的高效群体模板生成框架。该框架利用隐层表征的算术特性，能够扩展到任意规模的群体。InstantGroup采用具有共享权重孪生网络的双VAE主干结构以处理成对输入，并包含位移反演模块（DIM）以保持模板的无偏性，以及主体-模板对齐模块（STAM）以提升模板质量与配准精度。在OASIS与ADNI数据集的三维脑部MRI扫描上的实验表明，InstantGroup显著降低了运行时间，可在数秒内为不同规模群体生成模板，同时在无偏性与配准精度等定量指标上保持优于现有先进基线的性能。