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.

翻译：目的：眼部形态在人群中存在显著差异，尤其是眼眶和视神经区域。这些变异限制了将眼部器官的群体特征推广到无偏空间参考的可行性与鲁棒性。方法：为应对这些限制，我们提出一种创建高分辨率无偏眼部图谱的流程。首先，针对层间分辨率低于层内分辨率的扫描图像，我们采用基于深度学习的超分辨率算法以恢复空间细节。随后，我们使用小部分受试者扫描数据，通过基于度量的迭代配准生成初始无偏参考模板。将其余扫描数据配准至该模板后，采用无监督深度概率方法对模板进行优化，该方法通过生成更广域的形变场以提升器官边界对齐精度。我们利用四种不同组织对比度的磁共振图像验证该框架，在独立空间配准中生成四个图谱。结果：针对每种组织对比度，通过Wilcoxon符号秩检验发现，相较于由刚性、仿射及可变形变换组成的标准配准框架，四个标注区域的平均Dice分数均呈现显著提升。这些结果凸显了所提流程在眼部器官及边界对齐方面的有效性。结论：通过结合超分辨率预处理与深度概率模型，我们解决了为高度变异人群建立标准化眼部参考图谱的挑战。