Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, Pennsylvania 15212, USA.
Med Phys. 2009 Oct;36(10):4349-58. doi: 10.1118/1.3213099.
The application of automated segmentation methods for tumor delineation on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images presents an opportunity to reduce the interobserver variability in radiotherapy (RT) treatment planning. In this work, three segmentation methods were evaluated and compared for rectal and anal cancer patients: (i) Percentage of the maximum standardized uptake value (SUV% max), (ii) fixed SUV cutoff of 2.5 (SUV2.5), and (iii) mathematical technique based on a confidence connected region growing (CCRG) method. A phantom study was performed to determine the SUV% max threshold value and found to be 43%, SUV43% max. The CCRG method is an iterative scheme that relies on the use of statistics from a specified region in the tumor. The scheme is initialized by a subregion of pixels surrounding the maximum intensity pixel. The mean and standard deviation of this region are measured and the pixels connected to the region are included or not based on the criterion that they are greater than a value derived from the mean and standard deviation. The mean and standard deviation of this new region are then measured and the process repeats. FDG-PET-CT imaging studies for 18 patients who received RT were used to evaluate the segmentation methods. A PET avid (PETavid) region was manually segmented for each patient and the volume was then used to compare the calculated volumes along with the absolute mean difference and range for all methods. For the SUV43% max method, the volumes were always smaller than the PETavid volume by a mean of 56% and a range of 21%-79%. The volumes from the SUV2.5 method were either smaller or larger than the PETavid volume by a mean of 37% and a range of 2%-130%. The CCRG approach provided the best results with a mean difference of 9% and a range of 1%-27%. Results show that the CCRG technique can be used in the segmentation of tumor volumes on FDG-PET images, thus providing treatment planners with a clinically viable starting point for tumor delineation and minimizing the interobserver variability in radiotherapy planning.
自动分割方法在 18F-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）图像上进行肿瘤勾画，为放疗（RT）治疗计划中的观察者间变异性提供了减少的机会。在这项工作中，评估并比较了三种用于直肠和肛门癌患者的分割方法：（i）最大标准化摄取值（SUV% max）的百分比，（ii）固定 SUV 截止值 2.5（SUV2.5），和（iii）基于置信度连通区域生长（CCRG）方法的数学技术。进行了一项体模研究以确定 SUV% max 阈值，并发现为 43%，SUV43% max。CCRG 方法是一种迭代方案，依赖于肿瘤中指定区域的统计信息。该方案由围绕最大强度像素的像素的子区域初始化。测量该区域的平均值和标准偏差，并根据它们大于从平均值和标准偏差导出的值的准则来包括或不包括与该区域连接的像素。然后测量此新区域的平均值和标准偏差，并重复该过程。使用 18 名接受 RT 的患者的 FDG-PET-CT 成像研究来评估分割方法。为每位患者手动分割 PET 活性（PETavid）区域，然后使用该体积来比较计算的体积以及所有方法的绝对平均差异和范围。对于 SUV43% max 方法，体积始终比 PETavid 体积小，平均小 56%，范围为 21%-79%。SUV2.5 方法的体积要么小于，要么大于 PETavid 体积，平均值为 37%，范围为 2%-130%。CCRG 方法提供了最好的结果，平均差异为 9%，范围为 1%-27%。结果表明，CCRG 技术可用于 FDG-PET 图像上的肿瘤体积分割，从而为治疗计划者提供肿瘤勾画的临床可行起点，并最大限度地减少放疗计划中的观察者间变异性。
