IEEE Trans Med Imaging. 2019 Nov;38(11):2654-2664. doi: 10.1109/TMI.2019.2907072. Epub 2019 Apr 9.
Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This paper presents an analysis of atlas selection methods in the context of radiotherapy treatment planning. For a range of commonly contoured OARs, a thorough comparison of a large class of typical atlas selection methods has been performed. For this evaluation, clinically contoured CT images of the head and neck ( N=316 ) and thorax ( N=280 ) were used. The state-of-the-art intensity and deformation similarity-based atlas selection methods were found to compare poorly to perfect atlas selection. Counter-intuitively, atlas selection methods based on a fixed set of representative atlases outperformed atlas selection methods based on the patient image. This study suggests that atlas-based segmentation with currently available selection methods compares poorly to the potential best performance, hampering the clinical utility of atlas-based segmentation. Effective atlas selection remains an open challenge in atlas-based segmentation for radiotherapy planning.
基于图谱的自动分割被用于放射治疗计划中，以加速危及器官（OARs）的勾画。图谱选择被认为是提高分割准确性和执行时间的一种方法，假设图谱与患者越相似，结果就越好。本文在放射治疗计划的背景下分析了图谱选择方法。对于一系列常见的轮廓 OAR，我们对一大类典型的图谱选择方法进行了全面比较。在这项评估中，使用了临床勾画的头颈部（N=316）和胸部（N=280）CT 图像。基于最新的强度和变形相似性的图谱选择方法与完美的图谱选择相比表现不佳。违反直觉的是，基于固定的一组有代表性的图谱的图谱选择方法优于基于患者图像的图谱选择方法。这项研究表明，目前可用的选择方法的基于图谱的分割与潜在的最佳性能相比表现不佳，阻碍了基于图谱的分割在放射治疗计划中的临床应用。有效的图谱选择仍然是放射治疗计划中基于图谱的分割的一个开放性挑战。
