Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark.
Med Phys. 2021 Dec;48(12):7837-7849. doi: 10.1002/mp.15289. Epub 2021 Oct 29.
Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph-cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans.
The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi-atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in three planes. The centerlines are refined by applying the path tracking algorithm to curved multiplanar reformatted scans and are then smoothed and dilated nonuniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph-cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted PA bifurcation level. The method is evaluated on noncontrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intraclass correlation (ICC) between manual and automatic diameters, both measured in axial slices at the PA bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan-rescan pairs with an average in-between time of 3 months.
We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in-slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the ratio, and for automatic diameters in 3D volumes around the PA bifurcation level between scan and rescan was 0.89, 0.95, and 0.86, respectively.
The proposed automatic segmentation method can reliably extract diameters of the large arteries in non-ECG-gated noncontrast CT scans such as are acquired in lung cancer screening.
由于这些血管的直径和形状与多种心血管疾病以及慢性阻塞性肺疾病患者的恶化和死亡风险相关，因此准确分割肺动脉和主动脉非常重要。我们提出了一种基于最优表面图割算法的全自动方法，用于量化非对比 CT 扫描中肺动脉和主动脉的完整 3D 形状和直径。
该算法首先通过多图谱配准从右肺和左肺动脉、肺动脉干、升主动脉和降主动脉中提取种子点。随后，通过在提取的种子点之间进行最小成本路径跟踪，提取肺动脉和主动脉的中心线，成本基于三个平面的管腔强度相似性和多尺度中轴性的组合。将路径跟踪算法应用于弯曲的多平面重建扫描，以细化中心线，然后根据从中轴滤波器提取的局部血管半径不均匀地平滑和扩张。将得到的血管粗略估计用作图割分割的初始化。一旦分割了血管，就可以在单个轴向切片以及自动提取的肺动脉分叉水平周围的 10mm 体积内自动计算肺动脉（PA）和升主动脉（AA）的直径以及 比。该方法在丹麦肺癌筛查试验（DLCST）的非对比 CT 扫描中进行了评估。在 25 次 CT 扫描上，通过与手动注释进行比较来确定分割准确性。在另外 200 次 CT 扫描上，计算手动和自动直径（均在 PA 分叉水平的轴向切片上测量）之间的组内相关系数（ICC）。在 118 次扫描-重扫对中评估了自动 3D 体积直径和 比（垂直于血管轴）的重复性，平均两次扫描之间的时间间隔为 3 个月。
我们获得了 0.94±0.02 的肺动脉分割重叠率和 0.96±0.01 的主动脉分割重叠率，分别为 0.62±0.33mm 和 0.43±0.07mm。PA 手动和自动切片直径测量之间的 ICC 为 0.92，AA 为 0.97，比为 0.90，PA 分叉水平周围 3D 体积自动直径扫描和重扫之间的 ICC 分别为 0.89、0.95 和 0.86。
所提出的自动分割方法可以可靠地提取非 ECG 门控非对比 CT 扫描（如肺癌筛查中获得的）中大动脉的直径。
