A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
This work addresses template estimation for computational anatomy, particularly in brain imaging, but appears incremental as it builds on existing methods by integrating multiple inference steps.
The paper tackled the problem of template estimation in 3D brain images by proposing a generative model that simultaneously infers bias fields, deformations, and variance hyperparameters to account for variability in sites and acquisition protocols, with results on synthetic and real brain MRI images demonstrating its capability to capture intensity heterogeneity and provide reliable template estimation.
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods need to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration.