ARTORG Center for Biomedical Engineering Research, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern, Switzerland.
Int J Radiat Oncol Biol Phys. 2012 Nov 15;84(4):e541-7. doi: 10.1016/j.ijrobp.2012.05.040. Epub 2012 Aug 4.
Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors.
Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert.
Cross-validation revealed a dice similarity of 95%±2% for the sclera and cornea and 91%±2% for the lens. Overall, mean segmentation error was found to be 0.3±0.1 mm. Average segmentation time was 14±2 s on a standard personal computer.
Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.
眼部解剖结构和与辐射相关的毒性对外部束放射治疗提出了独特的挑战。为了进行治疗计划，对危及器官和肿瘤体积进行精确建模至关重要。由于器官形状的可变性，精确的眼部模型的开发以及该模型对患者解剖结构的自动适应仍然存在问题。这项工作介绍了三维（3D）统计形状模型的应用，该模型是一种用于眼部肿瘤的外部束放射治疗的精确眼部建模的新方法。
根据头 CT 容积扫描，对 17 例患者的手动和自动分割进行了比较。开发了角膜、晶状体和巩膜以及视盘位置的 3D 统计形状模型。此外，构建了一个主动形状模型，以实现对 CT 切片堆栈的眼部模型自动拟合。通过对所有训练形状进行逐个删除测试，基于 Dice 相似系数和专家手动分割与自动分割之间的平均分割误差来进行交叉验证。
交叉验证显示巩膜和角膜的 Dice 相似性为 95%±2%，晶状体为 91%±2%。总体而言，平均分割误差为 0.3±0.1mm。在标准个人计算机上的平均分割时间为 14±2s。
我们的结果表明，与现有的方法相比，该解决方案在准确性、可靠性和鲁棒性方面表现出色。此外，眼部模型的形状及其可变性是从训练集中学习的，而不是通过进行形状假设（例如，使用球形或椭圆形模型）。因此，该模型似乎能够对非球形和非椭圆形的眼睛进行建模。
