目的:这项研究的目的是评估共识算法在应用于临床PET图像时对分割结果的影响。特别是,研究了使用多数投票或STAPLE算法是否可以提高由三种半自动分割算法组合提供的分割的准确性和可重复性。
方法:三种已发布的分割方法(面向对比,可能性理论和自适应阈值)和两种共识算法(多数投票和STAPLE)在单个软件平台(Artiview®)中实现。四个临床数据集,包括不同的位置(胸部,乳房,腹部)或病理(原发性NSCLC肿瘤,转移,与病理学作为地面实况或CT作为地面实况替代相比,淋巴瘤)用于评估共识方法的准确性和可重复性。
结果:不同肿瘤实体病变的个体分割算法性能的变异性反映了PET图像在分辨率方面的变异性,对比和噪音。与病变的位置和病理无关,然而,在大多数情况下,与表现最差的单独方法相比,共识方法提高了体积分割的准确性,并且在许多情况下接近表现最好的方法.此外,实施显示,分割结果具有很高的可重复性,而各自的起始条件变化很小。结果与STAPLE算法和多数投票算法没有显著差异。
结论:本研究表明,通过使用一致性算法将不同的PET分割方法结合起来,对于单个分割方法的可变性能具有鲁棒性,因此这种方法在放射肿瘤学中很有用。它也可能与其他情况有关,例如临床常规和试验中专家建议的合并,或用于自动轮廓标准化的轮廓的多观察者生成。
OBJECTIVE: The aim of this study was to evaluate the impact of
consensus algorithms on segmentation results when applied to clinical PET images. In particular, whether the use of the majority vote or STAPLE algorithm could improve the accuracy and reproducibility of the segmentation provided by the combination of three semiautomatic segmentation algorithms was investigated.
METHODS: Three published segmentation methods (contrast-oriented, possibility theory and adaptive thresholding) and two
consensus algorithms (majority vote and STAPLE) were implemented in a single software platform (Artiview®). Four clinical datasets including different locations (thorax, breast, abdomen) or pathologies (primary NSCLC tumours, metastasis, lymphoma) were used to evaluate accuracy and reproducibility of the
consensus approach in comparison with pathology as the ground truth or CT as a ground truth surrogate.
RESULTS: Variability in the performance of the individual segmentation algorithms for lesions of different tumour entities reflected the variability in PET images in terms of resolution, contrast and noise. Independent of location and pathology of the lesion, however, the
consensus method resulted in improved accuracy in volume segmentation compared with the worst-performing individual method in the majority of cases and was close to the best-performing method in many cases. In addition, the implementation revealed high reproducibility in the segmentation results with small changes in the respective starting conditions. There were no significant differences in the results with the STAPLE algorithm and the majority vote algorithm.
CONCLUSIONS: This study showed that combining different PET segmentation methods by the use of a
consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendations in clinical routine and trials or the multiobserver generation of contours for standardization of automatic contouring.