Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction algorithms where it improves the accuracy of resulting CT numbers. Of special interest are pelvic bones, which—because of their strong attenuation—affect the accuracy of brachytherapy in this region. This work evaluated the performance of the JJ2016 algorithm with the performance of MK2014v2 and JS2018 algorithms; all these algorithms were developed by authors. Visual comparison, and, in the latter case, also Dice similarity coefficients derived from the ground truth were used. It was found that the 3D-based JJ2016 performed better than the 2D-based MK2014v2, mainly because of the more accurate hole filling that benefitted from information in adjacent slices. The neural network-based JS2018 outperformed both traditional algorithms. It was, however, limited to the resolution of 1283 owing to the limited amount of memory in the graphical processing unit (GPU).

中文翻译：

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用于机器学习数据集注释的 CT 体积中盆腔区域骨​​骼的半自动 3D 分割

计算机断层扫描 (CT) 图像中骨骼的自动分割用于例如光束硬化校正算法，其中它提高了所得 CT 数字的准确性。特别令人感兴趣的是骨盆骨，由于它们的强烈衰减，会影响该区域近距离放射治疗的准确性。这项工作评估了 JJ2016 算法的性能与 MK2014v2 和 JS2018 算法的性能；所有这些算法都是由作者开发的。视觉比较，在后一种情况下，也使用了从基本事实导出的骰子相似系数。发现基于 3D 的 JJ2016 比基于 2D 的 MK2014v2 表现更好，这主要是因为得益于相邻切片中的信息，更准确的孔填充。基于神经网络的 JS2018 优于两种传统算法。