灵活的分割评估方法：基于 CT 的行李扫描结果。
Flexible methods for segmentation evaluation: results from CT-based luggage screening.
机构信息
University of California, San Diego, CA, USA.
Lawrence Livermore National Laboratories, Livermore, CA, USA.
出版信息
J Xray Sci Technol. 2014;22(2):175-95. doi: 10.3233/XST-140418.
BACKGROUND
Imaging systems used in aviation security include segmentation algorithms in an automatic threat recognition pipeline. The segmentation algorithms evolve in response to emerging threats and changing performance requirements. Analysis of segmentation algorithms' behavior, including the nature of errors and feature recovery, facilitates their development. However, evaluation methods from the literature provide limited characterization of the segmentation algorithms.
OBJECTIVE
To develop segmentation evaluation methods that measure systematic errors such as oversegmentation and undersegmentation, outliers, and overall errors. The methods must measure feature recovery and allow us to prioritize segments.
METHODS
We developed two complementary evaluation methods using statistical techniques and information theory. We also created a semi-automatic method to define ground truth from 3D images. We applied our methods to evaluate five segmentation algorithms developed for CT luggage screening. We validated our methods with synthetic problems and an observer evaluation.
RESULTS
Both methods selected the same best segmentation algorithm. Human evaluation confirmed the findings. The measurement of systematic errors and prioritization helped in understanding the behavior of each segmentation algorithm.
CONCLUSIONS
Our evaluation methods allow us to measure and explain the accuracy of segmentation algorithms.
背景
航空安全中使用的成像系统包括自动威胁识别管道中的分割算法。分割算法会针对新兴威胁和不断变化的性能要求进行改进。分析分割算法的行为，包括错误和特征恢复的性质，可以促进其发展。但是，文献中的评估方法对分割算法的特征描述有限。
目的
开发分割评估方法，以测量过度分割和欠分割、异常值和整体误差等系统误差。这些方法必须测量特征恢复，并允许我们对段进行优先级排序。
方法
我们使用统计技术和信息论开发了两种互补的评估方法。我们还创建了一种半自动方法，从 3D 图像中定义真实情况。我们将我们的方法应用于评估为 CT 行李筛查开发的五种分割算法。我们使用合成问题和观察者评估来验证我们的方法。
结果
这两种方法都选择了相同的最佳分割算法。人体评估证实了这一发现。系统误差的测量和优先级排序有助于理解每个分割算法的行为。
结论
我们的评估方法使我们能够测量和解释分割算法的准确性。
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