IEEE Trans Med Imaging. 2019 Jan;38(1):99-106. doi: 10.1109/TMI.2018.2856464. Epub 2018 Jul 16.
Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be. This paper seeks to estimate a clinically achievable expected performance under this assumption. Assuming a perfect atlas selection, an extreme value theory has been applied to estimate the accuracy of single-atlas and multi-atlas segmentation given a large database of atlases. For this purpose, clinical contours of most common OARs on computed tomography of the head and neck ( N=316 ) and thoracic ( N=280 ) cases were used. This paper found that while for most organs, perfect segmentation cannot be reasonably expected, auto-contouring performance of a level corresponding to clinical quality could be consistently expected given a database of 5000 atlases under the assumption of perfect atlas selection.
基于图谱的分割被用于放射治疗计划中，以加速危险器官（OAR）的勾画。图谱选择被提出以改善分割性能，假设图谱与患者越相似，结果就越好。因此，从更大的图谱数据库中进行选择，结果应该会更好。本文旨在根据这一假设，估计临床可实现的预期性能。假设进行了完美的图谱选择，应用极值理论来估计在有大量图谱数据库的情况下，单图谱和多图谱分割的准确性。为此，使用了头部和颈部（N=316）和胸部（N=280）计算机断层扫描的大多数常见 OAR 的临床轮廓。本文发现，虽然对于大多数器官，无法合理地期望达到完美分割，但在假设进行了完美的图谱选择的情况下，给定一个 5000 个图谱的数据库，可始终期望获得与临床质量相当的自动勾画性能。
