Integrierte 4D-Segmentierung und Registrierung räumlich-zeitlicher Bildfolgen

时空图像序列的集成 4D 分割和配准

• 批准号: 66291222
• 负责人: Dr. Jan Ehrhardt
• 金额: --
• 依托单位: Institut für Medizinische Informatik (IMI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/66291222?language=en
• 关键词: Integrierte; 4D; Segmentierung; und; Registrierung

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.

医学图像的自动、稳健和可靠的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大影响。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，则当前可用的配准方法达到其极限。该项目的目的是即使在一对一的情况下也能够实现稳健且可靠的图像配准。 - 部分图像中缺少一个对应关系。为了实现这一目标，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）将被集成到概率性分层配准框架中。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缺少局部对应的区域，并客观评估局部配准结果的准确性和可靠性。所提出的方法创新显着扩展了图像配准算法的医学应用范围。例如，所提出的方法将促进和提高基于图像的后续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，以揭示病理组织的空间分布模式或神经元活动。