Integrierte 4D-Segmentierung und Registrierung räumlich-zeitlicher Bildfolgen

医学图像的自动、鲁棒和可靠配准是医学图像计算的核心问题，对图像引导诊断和治疗具有重要影响。如果图像中存在强烈的解剖或病理差异，并且部分图像中缺少相应的结构，则现有的配准方法将达到其极限。该项目的目的是使图像的鲁棒和可靠的配准，即使在部分图像中缺少一对一的对应关系。为了实现这一点，通过图像分析方法提取的附加信息，如器官分割、地标、局部图像特征等，将被整合到一个概率、层次配准框架中。该配准框架采用基于对应概率的方法对图像进行对齐。所开发的方法将能够同时登记和检测缺少当地对应的地区，并对当地登记结果的准确性和可靠性进行客观评估。提出的方法创新扩展了图像配准算法的医疗应用范围，显着。例如，所提出的方法将促进和提高基于图像的随访研究和临床监测、术前和术后图像比较以及基于图像的统计研究的质量，以揭示病理组织或神经元活动的空间分布模式。

The automatic, robust and reliable registration of medical images is a central problem in medical image computing with high impact on image-guided diagnostics and therapy. Currently available registration methods reach their limits, if strong anatomical or pathologic discrepancies are present in the images and corresponding structures are missing in parts of the images.The aim of this project is to enable the robust and reliable registration of images even if one-to-one correspondences are missing in parts of the images. To achieve this, additional information extracted by image analysis methods like organ segmentations, landmarks, local image features etc. will be integrated into a probabilistic, hierarchical registration framework. The registration framework uses an approach based on correspondence probabilities to align images. The methods to develop will enable the simultaneous registration and detection of areas with missing local correspondences as well as the objective assessment of the accuracy and reliability of the local registration results.The proposed methodical innovations extend the medical application spectrum of image registration algorithms, significantly. For example, the proposed method will facilitate and improve the quality of image-based follow-up studies and clinical monitoring, comparison of pre- and post-operative images as well as image-based statistical studies to reveal spatial distribution patterns of pathological tissues or neuronal activities.