Polzin T, Rühaak J, Werner R, Handels H, Modersitzki J
Thomas Polzin, Institute of Mathematics and Image Computing, University of Lübeck, Maria-Goeppert-Straße 3, 23562 Lübeck, Germany, E-mail:
Methods Inf Med. 2014;53(4):250-6. doi: 10.3414/ME13-01-0125. Epub 2014 Jul 4.
Accurate registration of lung CT images is inevitable for numerous clinical applications. Usually, nonlinear intensity-based methods are used. Their accuracy is typically evaluated using corresponding anatomical points (landmarks; e.g. bifurcations of bronchial and vessel trees) annotated by medical experts in the images to register. As image registration can be interpreted as correspondence finding problem, these corresponding landmarks can also be used in feature-based registration techniques. Recently, approaches for automated identification of such landmark correspondences in lung CT images have been presented. In this work, a novel combination of variational nonlinear intensity-based registration with an approach for automated landmark correspondence detection in lung CT pairs is presented and evaluated.
The main blocks of the proposed hybrid intensity- and feature-based registration scheme are a two-step landmark correspondence detection and the so-called CoLD (Combining Landmarks and Distance Measures) framework. The landmark correspondence identification starts with feature detection in one image followed by a blockmatching-based transfer of the features to the other image. The established correspondences are used to compute a thin-plate spline (TPS) transformation. Within CoLD, the TPS transformation is improved by minimization of an objective function consisting of a Normalized Gradient Field distance measure and a curvature regularizer; the landmark correspondences are guaranteed to be preserved by optimization on the kernel of the discretized landmark constraints.
Based on ten publicly available end-inspiration/expiration CT scan pairs with anatomical landmark sets annotated by medical experts from the DIR-Lab database, it is shown that the hybrid registration approach is superior in terms of accuracy: The mean distance of expert landmarks is decreased from 8.46 mm before to 1.15 mm after registration, outperforming both the TPS transformation (1.68 mm) and a nonlinear registration without usage of automatically detected landmarks (2.44 mm). The improvement is statistically significant in eight of ten datasets in comparison to TPS and in nine of ten datasets in comparison to the intensity-based registration. Furthermore, CoLD globally estimates the breathing-induced lung volume change well and results in smooth and physiologically plausible motion fields of the lungs.
We demonstrated that our novel landmark-based registration pipeline outperforms both TPS and the underlying nonlinear intensity-based registration without landmark usage. This highlights the potential of automatic landmark correspondence detection for improvement of lung CT registration accuracy.
对于众多临床应用而言，准确配准肺部CT图像是必不可少的。通常会使用基于强度的非线性方法。其准确性通常通过医学专家在待配准图像中标注的相应解剖点（地标；例如支气管和血管树的分支）来评估。由于图像配准可被解释为对应关系查找问题，这些相应的地标也可用于基于特征的配准技术。最近，已经提出了在肺部CT图像中自动识别此类地标对应关系的方法。在这项工作中，提出并评估了一种基于变分非线性强度的配准与肺部CT图像对中自动地标对应检测方法的新颖组合。
所提出的基于强度和特征的混合配准方案的主要模块是两步地标对应检测和所谓的CoLD（结合地标和距离度量）框架。地标对应识别首先在一幅图像中进行特征检测，然后基于块匹配将特征转移到另一幅图像。已建立的对应关系用于计算薄板样条（TPS）变换。在CoLD中，通过最小化由归一化梯度场距离度量和曲率正则化器组成的目标函数来改进TPS变换；通过在离散地标约束的核上进行优化，确保地标对应关系得以保留。
基于来自DIR-Lab数据库的十对公开可用的吸气末/呼气末CT扫描对，且带有医学专家标注的解剖地标集，结果表明混合配准方法在准确性方面更具优势：专家地标之间的平均距离从配准前的8.46毫米降至配准后的1.15毫米，优于TPS变换（1.68毫米）和未使用自动检测地标进行的非线性配准（2.44毫米）。与TPS相比，在十个数据集中有八个数据集的改进具有统计学意义；与基于强度的配准相比，在十个数据集中有九个数据集的改进具有统计学意义。此外，CoLD能够很好地全局估计呼吸引起的肺容积变化，并产生平滑且符合生理的肺部运动场。
我们证明了我们新颖的基于地标的配准流程优于TPS以及底层的未使用地标的基于强度的非线性配准。这突出了自动地标对应检测在提高肺部CT配准准确性方面的潜力。
