InstantGroup:即时模板生成可扩展组脑MRI注册

IF 13.7
Ziyi He;Albert C. S. Chung
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引用次数: 0

摘要

模板生成是图像分组配准的关键步骤,它涉及到将一组主题对齐到一个公共空间。虽然现有的方法可以生成高质量的模板图像,但它们通常会产生大量的时间成本或受到固定组尺度的限制。在本文中,我们提出了InstantGroup,一个有效的基于变分自编码器(VAE)模型的分组模板生成框架,该框架利用潜在表示的算术属性,使其能够扩展到任何大小的组。InstantGroup采用双VAE骨干网和共享权重的双网络来处理成对的输入,并采用位移反演模块(DIM)来保持模板的无偏性,并采用主题模板对齐模块(STAM)来提高模板质量和配准精度。对OASIS和ADNI数据集进行的3D脑MRI扫描实验表明,InstantGroup显著缩短了运行时间,在几秒钟内生成各种组大小的模板,同时与最先进的定量指标(包括无偏性和配准精度)基线相比,保持了卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration
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.
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