Department of Radiation Oncology, Emory University School of Medicine and Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA.
Int J Radiat Oncol Biol Phys. 2010 Jul 1;77(3):959-66. doi: 10.1016/j.ijrobp.2009.09.023. Epub 2010 Mar 16.
To evaluate if automatic atlas-based lymph node segmentation (LNS) improves efficiency and decreases inter-observer variability while maintaining accuracy.
Five physicians with head-and-neck IMRT experience used computed tomography (CT) data from 5 patients to create bilateral neck clinical target volumes covering specified nodal levels. A second contour set was automatically generated using a commercially available atlas. Physicians modified the automatic contours to make them acceptable for treatment planning. To assess contour variability, the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was used to take collections of contours and calculate a probabilistic estimate of the "true" segmentation. Differences between the manual, automatic, and automatic-modified (AM) contours were analyzed using multiple metrics.
Compared with the "true" segmentation created from manual contours, the automatic contours had a high degree of accuracy, with sensitivity, Dice similarity coefficient, and mean/max surface disagreement values comparable to the average manual contour (86%, 76%, 3.3/17.4 mm automatic vs. 73%, 79%, 2.8/17 mm manual). The AM group was more consistent than the manual group for multiple metrics, most notably reducing the range of contour volume (106-430 mL manual vs. 176-347 mL AM) and percent false positivity (1-37% manual vs. 1-7% AM). Average contouring time savings with the automatic segmentation was 11.5 min per patient, a 35% reduction.
Using the STAPLE algorithm to generate "true" contours from multiple physician contours, we demonstrated that, in comparison with manual segmentation, atlas-based automatic LNS for head-and-neck cancer is accurate, efficient, and reduces interobserver variability.
评估自动图谱配准淋巴结分割（LNS）是否可以提高效率，减少观察者间的变异性，同时保持准确性。
5 名具有头颈部调强放疗经验的医生使用 5 名患者的计算机断层扫描（CT）数据创建覆盖指定淋巴结水平的双侧颈部临床靶区。使用商业上可用的图谱自动生成第二套轮廓。医生修改自动轮廓，使其可用于治疗计划。为了评估轮廓的可变性，使用同时真实和性能水平估计（STAPLE）算法来获取轮廓集并计算“真实”分割的概率估计。使用多种指标分析手动、自动和自动修改（AM）轮廓之间的差异。
与从手动轮廓创建的“真实”分割相比，自动轮廓具有很高的准确性，灵敏度、Dice 相似系数和平均/最大表面差异值与平均手动轮廓相当（86%、76%、3.3/17.4mm 自动与 73%、79%、2.8/17mm 手动）。对于多个指标，AM 组比手动组更一致，特别是减少了轮廓体积的范围（手动 106-430ml 与 AM 176-347ml）和假阳性百分比（手动 1-37%与 AM 1-7%）。使用自动分割平均节省 11.5 分钟/患者，减少了 35%。
使用 STAPLE 算法从多个医生轮廓生成“真实”轮廓，我们证明与手动分割相比，头颈部癌症基于图谱的自动 LNS 是准确、高效的，并且减少了观察者间的变异性。
