Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Sciences Department, Université de Sherbrooke, Sherbrooke, Canada.
Université de Bordeaux, CNRS, CEA, IMN, GIN, UMR 5293, 33000, Bordeaux, France.
Sci Rep. 2023 Feb 9;13(1):2347. doi: 10.1038/s41598-023-28560-w.
Since 2015, research groups have sought to produce the ne plus ultra of tractography algorithms using the ISMRM 2015 Tractography Challenge as evaluation. In particular, since 2017, machine learning has made its entrance into the tractography world. The ISMRM 2015 Tractography Challenge is the most used phantom during tractography validation, although it contains limitations. Here, we offer a new scoring system for this phantom, where segmentation of the bundles is now based on manually defined regions of interest rather than on bundle recognition. Bundles are now more reliably segmented, offering more representative metrics for future users. New code is available online. Scores of the initial 96 submissions to the challenge are updated. Overall, conclusions from the 2015 challenge are confirmed with the new scoring, but individual tractogram scores have changed, and the data is much improved at the bundle- and streamline-level. This work also led to the production of a ground truth tractogram with less broken or looping streamlines and of an example of processed data, all available on the Tractometer website. This enhanced scoring system and new data should continue helping researchers develop and evaluate the next generation of tractography techniques.
自 2015 年以来，研究小组一直致力于使用 ISMRM 2015 追踪挑战赛作为评估来生成追踪算法的卓越版本。特别是自 2017 年以来，机器学习已经进入了追踪领域。ISMRM 2015 追踪挑战赛是追踪验证中最常用的幻象，但它存在一些局限性。在这里，我们为这个幻象提供了一个新的评分系统，其中束的分割现在基于手动定义的感兴趣区域，而不是基于束识别。束现在可以更可靠地分割，为未来的用户提供更具代表性的指标。新的代码可在线获取。挑战赛最初的 96 份提交的分数已更新。总体而言，新的评分系统证实了 2015 年挑战赛的结论，但个别追踪图的评分发生了变化，束和流线级别的数据得到了极大的改善。这项工作还产生了一个具有更少断裂或循环流线的真实追踪图，以及一个处理后的数据示例，这些都可以在 Tractometer 网站上获得。这个增强的评分系统和新的数据应该继续帮助研究人员开发和评估下一代追踪技术。
