Ecabert Olivier, Peters Jochen, Schramm Hauke, Lorenz Cristian, von Berg Jens, Walker Matthew J, Vembar Mani, Olszewski Mark E, Subramanyan Krishna, Lavi Guy, Weese Jürgen
Philips Research Europe-Aachen, X-ray ImagingSystems, Weisshausstr. 2, 52062 Aachen, Germany.
IEEE Trans Med Imaging. 2008 Sep;27(9):1189-201. doi: 10.1109/TMI.2008.918330.
Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.
自动图像处理方法是有效分析心脏检查期间计算机断层扫描（CT）扫描仪产生的大量图像数据的先决条件。本文介绍了一种基于模型的方法，用于从3D CT图像中全自动分割整个心脏（四个腔室、心肌和大血管）。通过逐步增加允许变形的自由度来进行模型适配。这提高了收敛性以及分割精度。首先使用广义霍夫变换的3D实现方法在图像中定位心脏。通过利用全局相似变换将模型与图像匹配来校正姿态失准。然后通过为模型的每个解剖区域分配仿射变换来解决多隔室网格的复杂初始化问题。最后，进行可变形适配以精确匹配患者解剖结构的边界。在对28幅图像进行的留一法定量验证中，测得平均表面到表面误差为0.82毫米。此外，为网格初始化和适配引入的分段仿射变换比常用的主成分分析显示出更好的相间和患者间形状变异性特征。
