IEEE Trans Med Imaging. 2014 Apr;33(4):836-48. doi: 10.1109/TMI.2013.2291711.
The goal of this work is to reliably and accurately localize anatomical landmarks in 3-D computed tomography scans, particularly for the deformable registration of whole-body scans, which show huge variation in posture, and the spatial distribution of anatomical features. Parts-based graphical models (GM) have shown attractive properties for this task because they capture naturally anatomical relationships between landmarks. Unfortunately, standard GMs are learned from manually annotated training images and the quantity of landmarks is limited by the high cost of expert annotation. We propose a novel method that automatically learns new corresponding landmarks from a database of 3-D whole-body CT scans, using a limited initial set of expert-labeled ground-truth landmarks. The newly learned landmarks, called B-landmarks, are used to build enriched GMs. We compare our method of deformable registration based on such GM landmarks to a conventional deformable registration method and to a "baseline" state-of-the-art GM. The results show our method finds new relevant anatomical correspondences and improves by up to 35% the matching accuracy of highly variable skeletal and soft-tissue landmarks of clinical interest.
这项工作的目标是可靠且准确地定位 3D 计算机断层扫描中的解剖学标志，特别是对于整个身体扫描的可变形配准，因为它们的姿势和解剖特征的空间分布有很大的差异。基于部件的图形模型（GM）因其能自然捕捉标志之间的解剖关系而具有吸引力。不幸的是，标准 GM 是从手动标注的训练图像中学习得到的，并且标志的数量受到专家标注成本高的限制。我们提出了一种新的方法，该方法可以从 3D 全身 CT 扫描数据库中自动学习新的对应标志，使用有限的初始专家标记的真实标志集。新学习的标志称为 B 标志，用于构建丰富的 GM。我们将基于这种 GM 标志的可变形配准方法与传统的可变形配准方法和“基线”最先进的 GM 进行比较。结果表明，我们的方法可以找到新的相关解剖对应关系，并将具有临床意义的高度可变骨骼和软组织标志的匹配精度提高多达 35%。
