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Clinical Evaluation of Commercial Atlas-Based Auto-Segmentation in the Head and Neck Region

机译:基于商业图集的头颈部区域自动分割的临床评价

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摘要

Background: While atlas segmentation (AS) has proven to be a time-saving and promising method for radiation therapy contouring, optimal methods for its use have not been well-established. Therefore, we investigated the relationship between the size of the atlas patient population and the atlas segmentation auto contouring (AC) performance.Methods: A total of 110 patients' head planning CT images were selected. The mandible and thyroid were selected for this study. The mandibles and thyroids of the patient population were carefully segmented by two skilled clinicians. Of the 110 patients, 100 random patients were registered to 5 different atlas libraries as atlas patients, in groups of 20 to 100, with increments of 20. AS was conducted for each of the remaining 10 patients, either by simultaneous atlas segmentation (SAS) or independent atlas segmentation (IAS). The AS duration of each target patient was recorded. To validate the accuracy of the generated contours, auto contours were compared to manually generated contours (MC) using a volume-overlap-dependent metric, Dice Similarity Coefficient (DSC), and a distance-dependent metric, Hausdorff Distance (HD).Results: In both organs, as the population increased from n = 20 to n = 60, the results showed better convergence. Generally, independent cases produced better performance than simultaneous cases. For the mandible, the best performance was achieved by n = 60 [DSC = 0.92 (0.01) and HD = 6.73 (1.31) mm] and the worst by n = 100 [DSC = 0.90 (0.03) and HD = 10.10 (6.52) mm] atlas libraries. Similar results were achieved with the thyroid; the best performance was achieved by n = 60 [DSC = 0.79 (0.06) and HD = 10.17 (2.89) mm] and the worst by n = 100 [DSC = 0.72 (0.13) and HD = 12.88 (3.94) mm] atlas libraries. Both IAS and SAS showed similar results. Manual contouring of the mandible and thyroid required an average of 1,044 (±170.15) seconds, while AS required an average of 46.4 (±2.8) seconds.Conclusions: The performance of AS AC generally increased as the population of the atlas library increased. However, the performance does not drastically vary in the larger atlas libraries in contrast to the logic that bigger atlas library should lead to better results. In fact, the results do not vary significantly toward the larger atlas library. It is necessary for the institutions to independently research the optimal number of subjects.
机译:背景:虽然地图集分割(AS)已被证明是一种节省时间且有希望的放射疗法轮廓绘制方法,但尚未很好地确定其最佳使用方法。因此,我们调查了图集患者人群的大小与图集分割自动轮廓(AC)性能之间的关系。方法:总共选择了110位患者的头部计划CT图像。选择下颌骨和甲状腺进行这项研究。两名熟练的临床医生对患者的下颌骨和甲状腺进行了仔细的分割。在这110名患者中,有100名随机患者被注册到5个不同的图集库中,作为图集患者,以20至100为一组,并以20为增量。通过同时进行图集分割(SAS)对其余10位患者中的每位进行AS或独立的地图集细分(IAS)。记录每个目标患者的AS持续时间。为了验证所生成轮廓的准确性,使用与体积重叠相关的度量,骰子相似系数(DSC)和与距离相关的度量,Hausdorff距离(HD),将自动轮廓与手动生成的轮廓(MC)进行比较。 strong>结果:在两个器官中,随着人口从n = 20增加到n = 60,结果显示出更好的收敛性。通常,独立案例比同时案例产生更好的性能。对于下颌骨,最佳性能是n = 60 [DSC = 0.92(0.01)和HD = 6.73(1.31)mm],最差的是n = 100 [DSC = 0.90(0.03)和HD = 10.10(6.52) [mm]地图集库。甲状腺取得了相似的结果。通过n = 60 [DSC = 0.79(0.06)和HD = 10.17(2.89)mm]可获得最佳性能,而通过n = 100 [DSC = 0.72(0.13)和HD = 12.88(3.94)mm]获得最佳性能。 IAS和SAS都显示出相似的结果。人工勾勒下颌骨和甲状腺的平均时间为1,044(±170.15)秒,而AS则平均需要46.4(±2.8)秒。结论: AS AC的性能通常随着人口的增长而提高地图集库的数量增加了。但是,与较大的Atlas库应导致更好的结果的逻辑相反,在较大的Atlas库中,性能并没有显着变化。实际上,对于较大的图集库,结果并没有显着差异。机构必须独立研究最佳学科数量。

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