Altunbas Cem, Howells Christopher, Proper Michelle, Reddy Krishna, Gan Gregory, DeWitt Peter, Kavanagh Brian, Schefter Tracey, Miften Moyed
Department of Radiation Oncology, University of Colorado, School of Medicine, Aurora, Colorado.
Department of Radiation Oncology, University of Colorado, School of Medicine, Aurora, Colorado.
Pract Radiat Oncol. 2014 Jul-Aug;4(4):217-25. doi: 10.1016/j.prro.2013.08.002. Epub 2013 Oct 10.
Image segmentation methods were studied to delineate liver lesions in (18)F-fluoro-2-deoxy-glucose positron emission tomographic (FDG-PET) images. The goal of this study was to identify a clinically practical, semiautomated FDG-PET avid volume segmentation method to improve the accuracy of liver tumor contouring for treatment planning in stereotactic body radiation therapy (SBRT).
Pretreatment PET-CT image sets for 26 patients who received SBRT to 28 liver lesions were delineated using the following 3 methods: (1) Percent threshold with respect to background corrected maximum standard uptake values (SUV; threshold values varied from 10% to 50% with 10% increments); (2) threshold 3 standard deviations above mean background SUV (3σ); and (3) a gradient-based method that detects the edge of the FDG-PET avid lesion (edge). For each lesion, semiautomatically generated contours were evaluated with respect to reference contours manually drawn by 3 radiation oncologists. Two similarity metrics, Dice coefficient, and mean minimal distance (MMD), were employed to assess the volumetric overlap and the mean Euclidian distance between semiautomatically and observer-drawn contours.
Mean Dice and MMD values for 10%, 20%, 30% threshold, 3σ, and edge varied from 0.69 to 0.73, and from 3.44 mm to 3.94 mm, respectively (ideal Dice and MMD values are 1 and 0 mm, respectively). A statistically significant difference was not observed among 10%, 20%, 30% threshold, 3σ, and edge methods, whereas 40% and 50% methods had inferior Dice and MMD values.
Three PET segmentation methods were identified above as potential tools to accelerate liver lesion delineation. The edge method appears to be the most practical for clinical implementation as it does not require calculation of SUV statistics. However, the performance of all segmentation methods showed large lesion-to-lesion fluctuations. Therefore, such methods may be suitable for generating initial estimates of FDG-PET avid volumes rather than being surrogates for manual volume delineation.
研究图像分割方法以在（18）F - 氟 - 2 - 脱氧 - 葡萄糖正电子发射断层扫描（FDG - PET）图像中勾勒肝脏病变。本研究的目的是确定一种临床实用的半自动FDG - PET高摄取体积分割方法，以提高立体定向体部放射治疗（SBRT）治疗计划中肝脏肿瘤轮廓勾画的准确性。
对26例接受SBRT治疗28个肝脏病变的患者的治疗前PET - CT图像集，使用以下3种方法进行勾勒：（1）相对于背景校正最大标准摄取值（SUV）的百分比阈值（阈值从10%到50%，以10%递增）；（2）高于平均背景SUV 3个标准差的阈值（3σ）；（3）一种基于梯度的方法，用于检测FDG - PET高摄取病变的边缘（边缘）。对于每个病变，针对由3名放射肿瘤学家手动绘制的参考轮廓，评估半自动生成的轮廓。采用两种相似性度量，即Dice系数和平均最小距离（MMD），来评估半自动轮廓与观察者绘制轮廓之间的体积重叠和平均欧几里得距离。
10%、20%、30%阈值、3σ和边缘方法的平均Dice值和MMD值分别在0.69至0.73以及3.44毫米至3.94毫米之间变化（理想的Dice值和MMD值分别为1和0毫米）。在10%、20%、30%阈值、3σ和边缘方法之间未观察到统计学上的显著差异，而40%和50%方法的Dice值和MMD值较差。
上述三种PET分割方法被确定为加速肝脏病变勾勒的潜在工具。边缘方法似乎是临床实施中最实用的，因为它不需要计算SUV统计量。然而，所有分割方法的性能在病变之间显示出较大波动。因此，此类方法可能适合生成FDG - PET高摄取体积的初始估计值，而不是作为手动体积勾勒的替代方法。
