Fast Predictive Medical Image Analysis

医学图像分析的目标是从图像中提取定量信息。图像配准是估计图像之间空间对应关系的一项关键图像分析技术。然而，虽然已经取得了良好的结果，但配准方法通常是缓慢的，特别是当要捕捉复杂的变形时。这限制了这些算法的用途(I)用于超大规模成像研究，(Ii)作为更高级分析算法的组件算法，以及(Iii)用于将受益于快速解决方案的应用，例如，以促进用户交互。此外，配准方法通常不适合给定的任务，因为仅为了数学上的方便，方法使用来自物理学的简单的弹性或流体模型。缺乏任务专用性会损害可实现的配准精度，即使对于最先进的算法也是如此。智能优点：因此，该项目将进一步开发、发明和研究基于快速、近似、学习的图像配准回归模型的快速分析方法，以取代昂贵的数值优化。使用这种学习的回归模型将促进以前由于计算限制而不容易实现的分析方法(例如，一般的大规模图像分析或使用变形距离来测量模型残差的图像的测地线回归方法)。该项目还将探索特定任务登记的回归模型(例如，纵向数据)，因此有可能实现超出目前最先进水平的登记精度。该项目的结果将对当前的脑成像研究产生直接影响，并将为高级脑成像数据和一般成像数据的分析奠定基础。更广泛的影响：虽然所提出的方法是以脑图像分析为动机的，但发明的方法将具有更广泛的适用性，例如分析腹部、肺部甚至非医学图像数据。为了灵活性和确保这些方法在其他应用领域的实用性，所有方法都将以开放源代码的形式提供给社区。这将允许其他人调整方法，复制结果，并创建定制的分析方法。为了简化结果的可解释性，我们将提供简单的可视化和不确定性量化方法，从而促进计算分析师和领域专家之间的沟通。

The goal of medical image analysis is to extract quantitative information from images. Image registration is a key image analysis technique to estimate spatial correspondences between images. However, while good results have been obtained, registration methods are typically slow, specifically, when complex deformations are to be captured. This limits the utility of these algorithms (i) for very large-scale imaging studies, (ii) as component algorithms of more advanced analysis algorithms, and (iii) for applications which would benefit from rapid solutions, for example, to facilitate user interaction. Furthermore, registration methods are typically ill-adapted to given tasks, as, for mathematical convenience only, approaches use simple elastic or fluid models from physics. This lack of task-specificity impairs achievable registration accuracy, even for state-of-the-art algorithms.Intellectual Merit: This project will therefore further develop, invent, and investigate fast analysis approaches based on replacing costly numerical optimizations by fast, approximate, learned regression models for image registration. Using such learned regression models will facilitate analysis approaches which were previously not easily possible due to computational constraints (for example, general large-scale image analysis or geodesic regression approaches for images which use deformation distances to measure model residuals}. This project will also explore regression models for task-specific registrations (for example, for longitudinal data) and will therefore open up the possibility to achieve registration accuracies beyond the current state-of-the-art. The project results will have immediate impact on current brain imaging studies and will form the basis for advanced analyses of brain and general imaging data.Broader Impact: While the proposed methods are motivated by the analysis of brain images, the invented methods will have more general applicability, e.g., to analyze abdominal, lung, or even non-medical image data. For flexibility and to assure utility of the approaches in other application domains, all methods will be made available to the community in open-source form. This will allow others to adapt approaches, to replicate results, and to create customized analysis approaches. To ease interpretability of results we will provide simple visualizations and approaches for uncertainty quantification, thereby facilitating communication between computational analysts and domain experts.