Fast Predictive Medical Image Analysis

快速预测医学图像分析

• 批准号: 1711776
• 负责人: Marc Niethammer
• 金额: $ 33万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711776&HistoricalAwards=false
• 关键词: Fast; Predictive; Medical; Image; Analysis

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.

医学图像分析的目标是从图像中提取定量信息。图像配准是一种重要的图像分析技术，用于估计图像之间的空间对应关系。然而，虽然已经获得了良好的结果，但配准方法通常很慢，特别是当要捕获复杂变形时。这限制了这些算法的实用性（i）用于非常大规模的成像研究，（ii）作为更高级的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，因为仅为了数学方便，方法使用来自物理学的简单弹性或流体模型。这种任务特异性的缺乏损害了可实现的配准精度，即使是最先进的algorithm.Intellectual Merit：因此，该项目将进一步开发，发明和研究快速分析方法的基础上取代昂贵的数值优化快速，近似，学习回归模型的图像配准。使用这种学习的回归模型将促进以前由于计算约束而不容易实现的分析方法（例如，使用变形距离来测量模型残差的用于图像的一般大规模图像分析或测地线回归方法）。本项目还将探索用于特定任务注册的回归模型（例如，纵向数据），因此将开辟实现超越当前最先进水平的配准精度的可能性。该项目的结果将对当前的脑成像研究产生直接影响，并将为脑和一般成像数据的高级分析奠定基础。虽然所提出的方法是由大脑图像的分析激发的，但是本发明的方法将具有更普遍的适用性，例如，以分析腹部、肺部或甚至非医学图像数据。为了灵活性，并确保在其他应用领域的方法的效用，所有的方法将提供给社区开放源代码的形式。这将使其他人能够调整方法，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。

项目成果

Local Temperature Scaling for Probability Calibration

• DOI: 10.1109/iccv48922.2021.00681
• 发表时间: 2020-08
• 期刊: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Zhipeng Ding;Xu Han;Peirong Liu;M. Niethammer

Votenet++: Registration Refinement For Multi-Atlas Segmentation

Votenet：多图集分割的注册细化

• DOI: 10.1109/isbi48211.2021.9434031
• 发表时间: 2021
• 期刊: International Symposium on Biomedical Imaging
• 作者: Ding, Zhipeng;Niethammer, Marc
• 通讯作者: Niethammer, Marc

Supplementary material for Fast Predictive Simple Geodesic Regression

快速预测简单测地线回归的补充材料

• 发表时间: 2019
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Ding, Z.;Fleishman, G.;Yang, X.;Thompson, P.;Kwitt, R.;Niethammer, M.
• 通讯作者: Niethammer, M.