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A quantitative evaluation function for 3D tree-like structure segmentations in liver images

机译：肝脏图像中3D树状结构分割的定量评估功能

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摘要

The analysis of vascular structure from volumetric datasets plays a crucial role in many medical applications. Many segmentation algorithms have been designed to extract the vessel features. However, to date, these algorithms have not been efficiently evaluated and a large-scale manual analysis is always impossible. In this paper, we propose a quantitative evaluation function based on connectivity, overlap volume ratio, skeleton coincidence and branches structure error to deal with this evaluation task. The function is applicable to 3D tree-like structure segmentations and does not depend on the segmentation algorithms used. The performances of this evaluation function are tested on real liver datasets. The results show that the values of evaluation function are very close to the human scoring value (standard value), and the average value of relative errors is only 7.3% over all the eight datasets, while other evaluation measurements are 20% or more. So, it provides the greatest correlation with human quality perception when compared with other evaluation measurements. Thus, it is the most suitable measure for the evaluation of 3D tree-like structure segmentations in liver images.
机译：来自体积数据集的血管结构分析在许多医学应用中起着至关重要的作用。已经设计了许多分割算法来提取血管特征。但是，迄今为止，尚未对这些算法进行有效评估，并且始终无法进行大规模的手动分析。在本文中，我们提出了一种基于连通性，重叠体积比，骨架重合和分支结构误差的定量评估函数来处理该评估任务。该功能适用​​于3D树状结构分割，并且不依赖于所使用的分割算法。此评估功能的性能在真实的肝脏数据集上进行了测试。结果表明，评估函数的值非常接近人类评分值（标准值），相对误差的平均值在全部八个数据集中均仅为7.3％，而其他评估测量值均在20％以上。因此，与其他评估度量相比，它与人类质量感知具有最大的相关性。因此，它是评估肝脏图像中3D树状结构分割的最合适方法。

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