  Purpose: Eye morphology varies significantly across the population,
especially for the orbit and optic nerve. These variations limit the
feasibility and robustness of generalizing population-wise features of eye
organs to an unbiased spatial reference.
  Approach: To tackle these limitations, we propose a process for creating
high-resolution unbiased eye atlases. First, to restore spatial details from
scans with a low through-plane resolution compared to a high in-plane
resolution, we apply a deep learning-based super-resolution algorithm. Then, we
generate an initial unbiased reference with an iterative metric-based
registration using a small portion of subject scans. We register the remaining
scans to this template and refine the template using an unsupervised deep
probabilistic approach that generates a more expansive deformation field to
enhance the organ boundary alignment. We demonstrate this framework using
magnetic resonance images across four different tissue contrasts, generating
four atlases in separate spatial alignments.
  Results: For each tissue contrast, we find a significant improvement using
the Wilcoxon signed-rank test in the average Dice score across four labeled
regions compared to a standard registration framework consisting of rigid,
affine, and deformable transformations. These results highlight the effective
alignment of eye organs and boundaries using our proposed process.
  Conclusions: By combining super-resolution preprocessing and deep
probabilistic models, we address the challenge of generating an eye atlas to
serve as a standardized reference across a largely variable population.
