论文标题
医学图像放射学研究中的可重复性：动态直方图的贡献
Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning
论文作者
论文摘要
对放射线特征提取的动态直方图套在一起的事实上，导致对注释区域波动的敏感性升高。这可能会影响最近发表的大多数放射组研究，并导致有关基于放射线的机器学习可重复性差的问题，这导致了大量的数据协调努力；但是，我们认为这里突出的问题被相对忽略，但通常通过选择静态箱来纠正。 通过开发社区标准和开源图书馆（例如墨拉哥），放射组学领域得到了改善。但是，图像获取，观察者注释之间的系统差异和预处理步骤的差异仍然带来挑战。这些可以改变改变提取特征的体素的分布，并可以通过动态套筒加剧。
The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libraries such as PyRadiomics. But differences in image acquisition, systematic differences between observers' annotations, and preprocessing steps still pose challenges. These can change the distribution of voxels altering extracted features and can be exacerbated with dynamic binning.
