Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Neuroimage. 2013 Jan 1;64:616-29. doi: 10.1016/j.neuroimage.2012.08.075. Epub 2012 Sep 4.
Volumetric measurements obtained from image parcellation have been instrumental in uncovering structure-function relationships. However, anatomical study of the cerebellum is a challenging task. Because of its complex structure, expert human raters have been necessary for reliable and accurate segmentation and parcellation. Such delineations are time-consuming and prohibitively expensive for large studies. Therefore, we present a three-part cerebellar parcellation system that utilizes multiple inexpert human raters that can efficiently and expediently produce results nearly on par with those of experts. This system includes a hierarchical delineation protocol, a rapid verification and evaluation process, and statistical fusion of the inexpert rater parcellations. The quality of the raters' and fused parcellations was established by examining their Dice similarity coefficient, region of interest (ROI) volumes, and the intraclass correlation coefficient of region volume. The intra-rater ICC was found to be 0.93 at the finest level of parcellation.
从图像分割中获得的容积测量在揭示结构-功能关系方面发挥了重要作用。然而，小脑的解剖研究是一项具有挑战性的任务。由于其复杂的结构，可靠和准确的分割和分区需要专家级的人类评分者。对于大型研究来说，这种描绘既耗时又昂贵。因此，我们提出了一个三部分的小脑分区系统，该系统利用多个非专业的人类评分者，可以高效和便捷地产生几乎与专家相同的结果。该系统包括一个分层的划分协议，一个快速的验证和评估过程，以及对非专业评分者的分区进行统计融合。通过检查评分者和融合分区的 Dice 相似系数、感兴趣区域（ROI）体积以及 ROI 体积的组内相关系数来确定分区的质量。在最细的分区水平上，评分者的组内相关系数为 0.93。
