Learning contrast-invariant contextual local descriptors and similarity metrics for multi-modal image registration

学习多模态图像配准的对比度不变上下文局部描述符和相似性度量

• 批准号: 320997906
• 负责人: Professor Dr. Mattias Heinrich
• 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: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/320997906?language=en
• 关键词: Learning; contrast; invariant; contextual; local

Deformable image registration is a key component for clinical imaging applications involving multi-modal image fusion, estimation of local deformations and image-guided interventions. A particular challenge for establishing correspondences between scans from different modalities: magnetic resonance imaging (MRI), computer tomography (CT) or ultrasound, is the definition of image similarity. Relying directly on intensity differences is not sufficient for most clinical images, which exhibit non-uniform changes in contrast, image noise, intensity distortions, artefacts, and globally non-linear intensity relations (for different modalities).In this project algorithms with increased robustness for medical image registration will be developed. We will improve on current state-of-the-art similarity measures by combining a larger number of versatile image features using simple local patch or histogram distances. Contrast-invariance and strong discrimination between corresponding and non-matching regions will be reached by capturing contextual information through pair-wise comparisons within an extended spatial neighbourhood of each voxel. Recent advances in machine learning will be used to learn problem-specific binary descriptors in a semi-supervised manner that can improve upon hand-crafted features by including a priori knowledge. Metric learning and higher-order mutual information will be employed for finding mappings between feature vectors across scans in order to reveal new relations among feature dimensions. Employing binary descriptors and sparse feature selection will improve computational efficiency (because it enables the use of the Hamming distance), while maintaining the robustness of the proposed methods.A deeper understanding of models for image similarity will be reached during the course of this project. The development of new methods for currently challenging (multi-modal) medical image registration problems will open new perspectives of computer-aided applications in clinical practice, including multi-modal diagnosis, modality synthesis, and image-guided interventions or radiotherapy.

可变形图像配准是涉及多模态图像融合、局部变形估计和图像引导干预的临床成像应用的关键组成部分。在来自不同模态（磁共振成像（MRI）、计算机断层扫描（CT）或超声）的扫描之间建立对应关系的一个特殊挑战是图像相似性的定义。直接依赖于强度差异是不够的，大多数临床图像，表现出非均匀的变化，对比度，图像噪声，强度失真，伪影，和全球非线性强度关系（不同的模态）。我们将通过使用简单的局部补丁或直方图距离结合大量的通用图像特征来改进当前最先进的相似性度量。通过在每个体素的扩展空间邻域内进行成对比较来捕获上下文信息，将实现对应区域和非匹配区域之间的对比度不变性和强区分。机器学习的最新进展将用于以半监督的方式学习特定于问题的二进制描述符，该方式可以通过包含先验知识来改进手工制作的特征。度量学习和高阶互信息将被用于寻找跨扫描的特征向量之间的映射，以揭示特征维度之间的新关系。采用二进制描述符和稀疏特征选择将提高计算效率（因为它允许使用汉明距离），同时保持所提出方法的鲁棒性。在本项目的过程中，将对图像相似性模型有更深入的理解。目前具有挑战性的（多模态）医学图像配准问题的新方法的开发将打开计算机辅助应用在临床实践中的新视角，包括多模态诊断，模态合成，图像引导的干预或放射治疗。