Integrierte 4D-Segmentierung und Registrierung räumlich-zeitlicher Bildfolgen

医学图像的自动、鲁棒和可靠的配准是医学图像计算中的核心问题，对图像引导的诊断和治疗有很大影响。目前可用的配准方法达到他们的极限，如果强大的解剖或病理差异存在于图像中，相应的结构在部分images.The项目的目的是使强大的和可靠的图像配准，即使一对一的对应关系在部分图像中丢失。为了实现这一点，通过图像分析方法提取的额外信息，如器官分割，地标，局部图像特征等，将被集成到一个概率，分层配准框架。配准框架使用基于对应概率的方法来对齐图像。开发的方法将能够同时配准和检测的区域与丢失的本地对应，以及客观评估的准确性和可靠性的本地registration results.The建议的方法创新扩展的医学应用范围的图像配准算法，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的质量，术前和术后图像的比较以及基于图像的统计研究，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.