Trampert Patrick, Bogachev Sviatoslav, Marniok Nico, Dahmen Tim, Slusallek Philipp
1German Research Center for Artificial Intelligence GmbH (DFKI),66123 Saarbrücken,Germany.
2Saarland University,66123 Saarbrücken,Germany.
Microsc Microanal. 2015 Dec;21(6):1591-1601. doi: 10.1017/S1431927615015433. Epub 2015 Nov 25.
We conducted a comparative study of three widely used algorithms for the detection of fiducial markers in electron microscopy images. The algorithms were applied to four datasets from different sources. For the purpose of obtaining comparable results, we introduced figures of merit and implemented all three algorithms in a unified code base to exclude software-specific differences. The application of the algorithms revealed that none of the three algorithms is superior to the others in all cases. This leads to the conclusion that the choice of a marker detection algorithm highly depends on the properties of the dataset to be analyzed, even within the narrowed domain of electron tomography.
我们对三种广泛用于检测电子显微镜图像中基准标记的算法进行了比较研究。这些算法被应用于来自不同来源的四个数据集。为了获得可比的结果，我们引入了品质因数，并在统一的代码库中实现了所有三种算法，以排除软件特定的差异。算法的应用表明，在所有情况下，这三种算法都不优于其他算法。由此得出结论，即使在电子断层扫描这个较窄的领域内，标记检测算法的选择也高度依赖于待分析数据集的属性。
