Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada.
Med Image Anal. 2015 Dec;26(1):287-305. doi: 10.1016/j.media.2015.10.011. Epub 2015 Nov 10.
Many different tractography approaches and corresponding isolated evaluation attempts have been presented over the last years, but a comparative and quantitative evaluation of tractography algorithms still remains a challenge, particularly in-vivo. The recently presented evaluation framework Tractometer is the first attempt to approach this challenge in a quantitative, comparative, persistent and open-access way. Tractometer is currently based on the evaluation of several global connectivity and tract-overlap metrics on hardware phantom data. The work presented in this paper focuses on extending Tractometer with a metric that enables the assessment of the local consistency of tractograms with the underlying image data that is not only applicable to phantom dataset but allows the quantitative and purely data-driven evaluation of in-vivo tractography. We furthermore present an extensive reference-based evaluation study of 25,000 tractograms obtained on phantom and in-vivo datasets using the presented local metric as well as all the methods already established in Tractometer. The experiments showed that the presented local metric successfully reflects the behavior of in-vivo tractography under different conditions and that it is consistent with the results of previous studies. Additionally our experiments enabled a multitude of conclusions with implications for fiber tractography in general, including recommendations regarding optimal choice of a local modeling technique, tractography algorithm, and parameterization, confirming and complementing the results of earlier studies.
在过去的几年中，已经提出了许多不同的束流追踪方法和相应的孤立评估尝试，但束流追踪算法的比较和定量评估仍然是一个挑战，特别是在体内。最近提出的评估框架 Tractometer 是首次尝试以定量、比较、持久和开放访问的方式解决这一挑战。Tractometer 目前基于对硬件体模数据上的几个全局连通性和束流重叠度量的评估。本文的工作重点是在 Tractometer 中扩展一个度量标准，该度量标准能够评估束流追踪与基础图像数据的局部一致性，该度量不仅适用于体模数据集，而且允许对体内束流追踪进行定量和纯数据驱动的评估。我们还使用提出的局部度量以及 Tractometer 中已经建立的所有方法，对来自体模和体内数据集的 25000 个束流追踪进行了广泛的基于参考的评估研究。实验表明，所提出的局部度量能够成功地反映不同条件下体内束流追踪的行为，并且与先前研究的结果一致。此外，我们的实验还得出了许多结论，这些结论对纤维束追踪具有普遍意义，包括关于局部建模技术、束流追踪算法和参数化的最佳选择的建议，这些结论证实和补充了早期研究的结果。
