Kurugol Sila, San Jose Estepar Raul, Ross James, Washko George R
Dept. of Pulmonary and Critical Care, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2343-6. doi: 10.1109/EMBC.2012.6346433.
Automatic aorta segmentation in thoracic computed tomography (CT) scans is important for aortic calcification quantification and to guide the segmentation of other central vessels. We propose an aorta segmentation algorithm consisting of an initial boundary detection step followed by 3D level set segmentation for refinement. Our algorithm exploits aortic cross-sectional circularity: we first detect aorta boundaries with a circular Hough transform on axial slices to detect ascending and descending aorta regions, and we apply the Hough transform on oblique slices to detect the aortic arch. The centers and radii of circles detected by Hough transform are fitted to smooth cubic spline functions using least-squares fitting. From these center and radius spline functions, we reconstruct an initial aorta surface using the Frenet frame. This reconstructed tubular surface is further refined with 3D level set evolutions. The level set framework we employ optimizes a functional that depends on both edge strength and smoothness terms and evolves the surface to the position of nearby edge location corresponding to the aorta wall. After aorta segmentation, we first detect the aortic calcifications with thresholding applied to the segmented aorta region. We then filter out the false positive regions due to nearby high intensity structures. We tested the algorithm on 45 CT scans and obtained a closest point mean error of 0.52 ± 0.10 mm between the manually and automatically segmented surfaces. The true positive detection rate of calcification algorithm was 0.96 over all CT scans.
在胸部计算机断层扫描（CT）中自动分割主动脉对于主动脉钙化定量以及指导其他中心血管的分割非常重要。我们提出了一种主动脉分割算法，该算法由初始边界检测步骤和随后用于细化的三维水平集分割组成。我们的算法利用了主动脉横截面的圆形特征：我们首先在轴向切片上使用圆形霍夫变换检测主动脉边界，以检测升主动脉和降主动脉区域，并在斜向切片上应用霍夫变换检测主动脉弓。通过最小二乘法拟合，将霍夫变换检测到的圆的中心和半径拟合为平滑的三次样条函数。根据这些中心和半径样条函数，我们使用弗伦内特标架重建初始主动脉表面。这个重建的管状表面通过三维水平集演化进一步细化。我们采用的水平集框架优化了一个依赖于边缘强度和平滑项的泛函，并将表面演化到与主动脉壁相对应的附近边缘位置。在主动脉分割之后，我们首先通过对分割后的主动脉区域应用阈值来检测主动脉钙化。然后，我们滤除由于附近高强度结构导致的假阳性区域。我们在45例CT扫描上测试了该算法，在手动分割和自动分割的表面之间获得了0.52±0.10毫米的最接近点平均误差。在所有CT扫描中，钙化算法的真阳性检测率为0.96。
