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使用多图谱方法对肺癌放疗中的臂丛神经进行自动轮廓勾画。

Automatic contouring of brachial plexus using a multi-atlas approach for lung cancer radiotherapy.

作者信息

Yang Jinzhong, Amini Arya, Williamson Ryan, Zhang Lifei, Zhang Yongbin, Komaki Ritsuko, Liao Zhongxing, Cox James, Welsh James, Court Laurence, Dong Lei

机构信息

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.

出版信息

Pract Radiat Oncol. 2013 Oct 1;3(4). doi: 10.1016/j.prro.2013.01.002.

Abstract

PURPOSE

To demonstrate a multi-atlas segmentation approach to facilitating accurate and consistent delineation of low-contrast brachial plexuses on CT images for lung cancer radiotherapy.

MATERIALS AND METHODS

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 (STAPLE) 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.

RESULTS

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 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.

CONCLUSIONS

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.

摘要

目的

展示一种多图谱分割方法,以促进在肺癌放疗的CT图像上准确且一致地勾勒出低对比度的臂丛神经。

材料与方法

我们回顾性地确定了90例治疗体积靠近臂丛神经的肺癌患者。选择10例具有代表性的患者组成图谱组,并手动勾勒出他们的臂丛神经。我们使用可变形图像配准将每个图谱中的臂丛神经映射到其余80例患者身上。在每位患者中,使用同时真相与性能水平估计(STAPLE)算法从10个单独的分割结果创建一个复合轮廓。对该自动勾勒的轮廓进行审查,并针对每位患者进行适当修改。我们还使用这10个图谱进行了10次留一法测试,以验证分割准确性并展示使用多图谱分割的轮廓一致性。

结果

多图谱分割完成时间不到2分钟。轮廓修改耗时5分钟,而从头开始手动勾勒轮廓则需要20分钟。10次留一法测试的多图谱分割的平均三维体积重叠率为59.2%±8.2%,平均三维表面距离为2.4毫米±0.5毫米。10次留一法测试中个体轮廓与平均轮廓之间的距离表明,修改后的轮廓比手动轮廓具有更好的轮廓一致性。自动分割的轮廓不需要大量修改,80例患者中修改后的轮廓与自动分割的轮廓之间的良好一致性证明了这一点。自动分割轮廓和修改后轮廓的剂量体积直方图也具有良好的一致性,表明从剂量学影响来看,编辑自动分割的轮廓在临床上是可接受的。

结论

多图谱分割大大减少了轮廓勾勒时间并提高了轮廓一致性。事实证明,编辑自动分割的轮廓以勾勒臂丛神经比从头开始手动勾勒轮廓是更好的临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cebd/3833708/11de9cd6f5d0/nihms434590f1.jpg

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