基于深度概率优化的超分辨率多对比度无偏眼部图谱
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
AI总结：
针对眼部形态差异大导致特征泛化困难的问题，提出结合深度学习超分辨率与无监督深度概率优化的方法，构建高分辨率多对比度无偏眼部图谱，显著提升了器官边界对齐精度。
摘要：
目的：眼部形态在人群中差异显著，尤其是眼眶和视神经。这些差异限制了将人群层面的眼部器官特征泛化到无偏空间参考的可行性和鲁棒性。方法：为了解决这些局限性，我们提出了一种创建高分辨率无偏眼部图谱的流程。首先，为了从层间分辨率低于层内分辨率的扫描中恢复空间细节，我们应用了基于深度学习的超分辨率算法。然后，我们使用少量受试者扫描进行基于度量的迭代配准，生成初始无偏参考。我们将剩余扫描配准到该模板，并使用无监督深度概率方法优化模板，该方法生成更广泛的形变场以增强器官边界对齐。我们利用四种不同组织对比度的磁共振图像演示该框架，在独立的空间对齐中生成四个图谱。结果：对于每种组织对比度，与包含刚性、仿射和可变形变换的标准配准框架相比，我们在四个标注区域的平均Dice分数上使用Wilcoxon符号秩检验发现了显著改善。这些结果突显了使用我们提出的流程对眼部器官和边界的有效对齐。结论：通过结合超分辨率预处理和深度概率模型，我们解决了生成眼部图谱以作为高度可变人群标准化参考的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
