Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer
Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer
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DOI：
10.1118/1.4927567
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发表时间：
2015-09-01
期刊：
影响因子：
3.8
通讯作者：
Ourselin, Sebastien
中科院分区：
文献类型：
--
作者：
Duc, Albert K. Hoang;Eminowicz, Gemma;Ourselin, Sebastien
目的：本研究的目的是评估使用新的“传播分割的相似性和真值估计”（STEPS）与传统的“同时真值和性能水平估计”（STAPLE）算法相比，是否可以自动有效地获得头颈部癌中临床可接受的危险器官（OARs）分割。方法：首先，2名放射肿瘤学家对100名头颈癌患者的数据集中的6个OARs进行了规划计算机断层扫描图像的轮廓。然后使用这些手动轮廓对数据集中的每个图像进行STAPLE和STEPS自动分割。然后用骰子相似系数（DSC）来比较这些自动方法的准确率。其次，在一项盲法实验中，三位独立且不同的训练有素的医生将手工和自动分割分为以下三个等级之一：根据通用描绘指南确定的临床可接受（a级），在手工编辑后临床实践中合理可接受（B级），不可接受（C级）。最后选择分级为B级的STEPS片段，由一名医生手工编辑至a级。记录编辑时间。结果：与STAPLE算法相比，在脑干、椎管、左右腮腺等大结构上使用STEPS算法可显著改善DSC（均p < 0.001）。此外，在所有三位训练有素的医生中，脑干、椎管、腮腺（右/左）和视交叉的手工和STEPS分割等级无显著差异（均p < 0.01）。相比之下，眼睛的STEPS分割等级较低（p < 0.001）。在所有的桨和所有的医生中，STEPS以83%的比率生成了分级分割和手动轮廓，这一比率的下限为80%，置信度为95%。当自动分割需要手工编辑和不需要手工编辑时，人工交互时间平均减少了61%和93%。结论：在头颈部放射治疗中，STEPS算法对OARs的分割效果优于STAPLE算法。它可以自动生成临床可接受的桨叶分割，其结果与手动绘制脑干、椎管、腮腺（左/右）和视交叉的轮廓一样相关。当使用STEPS时，即使是在需要手工编辑的情况下，也大大减少了手工劳动。(C) 2015年美国医学物理学家协会。
Purpose: The aim of this study was to assess whether clinically acceptable segmentations of organs at risk (OARs) in head and neck cancer can be obtained automatically and efficiently using the novel "similarity and truth estimation for propagated segmentations" (STEPS) compared to the traditional "simultaneous truth and performance level estimation" (STAPLE) algorithm.Methods: First, 6 OARs were contoured by 2 radiation oncologists in a dataset of 100 patients with head and neck cancer on planning computed tomography images. Each image in the dataset was then automatically segmented with STAPLE and STEPS using those manual contours. Dice similarity coefficient (DSC) was then used to compare the accuracy of these automatic methods. Second, in a blind experiment, three separate and distinct trained physicians graded manual and automatic segmentations into one of the following three grades: clinically acceptable as determined by universal delineation guidelines (grade A), reasonably acceptable for clinical practice upon manual editing (grade B), and not acceptable (grade C). Finally, STEPS segmentations graded B were selected and one of the physicians manually edited them to grade A. Editing time was recorded.Results: Significant improvements in DSC can be seen when using the STEPS algorithm on large structures such as the brainstem, spinal canal, and left/right parotid compared to the STAPLE algorithm (all p < 0.001). In addition, across all three trained physicians, manual and STEPS segmentation grades were not significantly different for the brainstem, spinal canal, parotid (right/left), and optic chiasm (all p > 0.100). In contrast, STEPS segmentation grades were lower for the eyes (p < 0.001). Across all OARs and all physicians, STEPS produced segmentations graded as well as manual contouring at a rate of 83%, giving a lower bound on this rate of 80% with 95% confidence. Reduction in manual interaction time was on average 61% and 93% when automatic segmentations did and did not, respectively, require manual editing.Conclusions: The STEPS algorithm showed better performance than the STAPLE algorithm in segmenting OARs for radiotherapy of the head and neck. It can automatically produce clinically acceptable segmentation of OARs, with results as relevant as manual contouring for the brainstem, spinal canal, the parotids (left/right), and optic chiasm. A substantial reduction in manual labor was achieved when using STEPS even when manual editing was necessary. (C) 2015 American Association of Physicists in Medicine.