The University of Iowa Roy and Lucille Carver College of Medicine, Department of Psychiatry, Iowa City, IA 52242, USA.
Neuroimage. 2011 Jan 1;54(1):328-36. doi: 10.1016/j.neuroimage.2010.06.047. Epub 2010 Jun 25.
The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.
BRAINS(大脑研究:图像、网络和系统分析)图像分析软件已经使用了 20 多年,并在不断发展。最初使用 BRAINS 的神经影像学分析流水线是一个半自动程序,用于测量大脑叶和皮质下结构的体积,在该过程的几个阶段需要手动干预。通过使用先进的图像处理算法,在图像配准、组织采样和掩模编辑等阶段的手动干预已经被消除。此外,还添加了不均匀性校正、强度归一化和掩模清理例程,以提高结果的准确性和一致性。在这项研究中,全自动方法 AutoWorkup 被证明比 18 位手动评估者的平均水平更可靠(所有组织在所有区域的 ICC≥0.96、Jaccard 指数≥0.80 和 Dice 指数≥0.89)。在一组 1130 个高质量扫描中,正确配准的失败率为 1.1%,需要对 4%的扫描进行大脑掩模的手动编辑。在其他测试中,AutoWorkup 被证明可生成可靠的测量结果,适用于跨扫描仪、扫描仪供应商和序列采集的数据。对来自 32 个站点、多供应商的 PREDICT-HD 研究的 AutoWorkup 分析数据的应用表明,所有组织和区域的可靠性估计值均大于或等于 0.90。