Super-Resolution Multi-Contrast Unbiased Eye Atlases With Deep
  Probabilistic Refinement
Ho Hin Lee Adam M. Saunders Michael E. Kim Samuel W. Remedios Yucheng Tang Qi Yang Xin Yu Shunxing Bao Chloe Cho Louise A. Mawn Tonia S. Rex Kevin L. Schey Blake E. Dewey Jeffrey M. Spraggins Jerry L. Prince Yuankai Huo Bennett A. Landman
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. 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 MRI tissue contrasts,
generating four atlases in separate spatial alignments. For each tissue
contrast, we find a significant improvement 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. 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.