Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
University of California Irvine School of Medicine, Irvine, California; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Pract Radiat Oncol. 2013 Oct-Dec;3(4):e139-47. doi: 10.1016/j.prro.2013.01.002. Epub 2013 Feb 9.
To demonstrate a multi-atlas segmentation approach to facilitating accurate and consistent delineation of low-contrast brachial plexuses on computed tomographic images for lung cancer radiation therapy.
We retrospectively identified 90 lung cancer patients with treatment volumes near the brachial plexus. Ten representative patients were selected to form an atlas group, and their brachial plexuses were delineated manually. We used deformable image registration to map each atlas brachial plexus to the remaining 80 patients. In each patient, a composite contour was created from 10 individual segmentations using the simultaneous truth and performance level estimation algorithm. This auto-delineated contour was reviewed and modified appropriately for each patient. We also performed 10 leave-one-out tests using the 10 atlases to validate the segmentation accuracy and demonstrate the contouring consistency using multi-atlas segmentation.
The multi-atlas segmentation took less than 2 minutes to complete. Contour modification took 5 minutes compared with 20 minutes for manual contouring from scratch. The multi-atlas segmentation from the 10 leave-one-out tests had a mean 3-dimensional (3D) volume overlap of 59.2% ± 8.2% and a mean 3D surface distance of 2.4 mm ± 0.5 mm. The distances between the individual and average contours in the 10 leave-one-out tests demonstrated much better contouring consistency for modified contours than for manual contours. The auto-segmented contours did not require substantial modification, demonstrated by the good agreement between the modified and auto-segmented contours in the 80 patients. Dose volume histograms of auto-segmented and modified contours were also in good agreement, showing that editing auto-segmented contours is clinically acceptable in view of the dosimetric impact.
Multi-atlas segmentation greatly reduced contouring time and improved contouring consistency. Editing auto-segmented contours to delineate the brachial plexus proved to be a better clinical practice than manually contouring from scratch.
展示一种多图谱分割方法,以促进在肺癌放射治疗的计算机断层图像上准确且一致地勾画低对比度臂丛神经。
我们回顾性地确定了 90 例肺癌患者,其治疗体积靠近臂丛神经。选择 10 名有代表性的患者组成图谱组,并手动勾画他们的臂丛神经。我们使用可变形图像配准将每个图谱臂丛神经映射到其余 80 名患者。在每个患者中,使用同时真实和性能水平估计算法从 10 个个体分割中创建一个复合轮廓。适当地审查和修改了每个患者的自动勾画轮廓。我们还使用 10 个图谱进行了 10 次留一法验证测试,以验证分割准确性,并展示多图谱分割的勾画一致性。
多图谱分割不到 2 分钟即可完成。与从头开始手动勾画轮廓相比,轮廓修改需要 5 分钟。10 次留一法验证测试的多图谱分割的平均三维(3D)体积重叠率为 59.2%±8.2%,平均 3D 表面距离为 2.4mm±0.5mm。在 10 次留一法验证测试中,个人轮廓和平均轮廓之间的距离表明,修改后的轮廓比手动轮廓的勾画一致性要好得多。自动分割的轮廓不需要进行大量修改,这表明在 80 名患者中,修改后的轮廓与自动分割的轮廓之间具有良好的一致性。自动分割和修改后的轮廓的剂量体积直方图也非常吻合,表明从剂量学角度来看,编辑自动分割的轮廓是可以接受的临床实践。
多图谱分割大大减少了勾画时间,提高了勾画一致性。通过编辑自动分割的轮廓来勾画臂丛神经被证明是一种比从头开始手动勾画更好的临床实践。