Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease.
Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease.
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DOI：
10.3389/fneur.2017.00519
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发表时间：
2017
影响因子：
3.4
通讯作者：
Scahill RI
中科院分区：
文献类型：
--
作者：
Johnson EB;Gregory S;Johnson HJ;Durr A;Leavitt BR;Roos RA;Rees G;Tabrizi SJ;Scahill RI
选择合适的分割工具是任何研究人员面临的挑战，旨在测量灰质（GM）体积。已经比较了许多工具，但目前还没有一种方法可以推荐高于所有其他方法；特别是，在疾病队列中缺乏验证。这项工作利用临床数据集对分割工具进行了广泛的比较。我们的研究结果证实，所有的工具都有优点和缺点，我们提出了一系列的考虑因素，可能是在选择通用分割方法时使用的，而不是这些工具的排名。使用来自20名对照、40名先兆亨廷顿舞蹈病（HD）和40名早期亨廷顿舞蹈病参与者的3t MRI数据，对7种分割工具进行了比较。分割的体块进行了详细的视觉质量控制。在重复基线扫描中研究了总GM、皮质GM和小叶GM的可靠性和可重复性。每个工具之间的关系也被检查。通过广义最小二乘回归评估3年以上组内纵向变化，以确定每种工具对疾病影响的敏感性。视觉质量控制和原始体积突出了工具之间的巨大差异，特别是在枕部和颞部区域。大多数工具表现出可靠的性能，并且体积通常是相关的。纵向组内变化的结果因工具而异，尤其是小叶区域。这些差异强调了在临床神经影像学研究中仔细选择分割方法的必要性。本指南作为针对新手或非技术成像科学家的入门读物，为选择适合队列的GM分割软件提供建议。
The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington’s disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software.
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影响因子：
4.8
作者：
Iscan Z;Jin TB;Kendrick A;Szeglin B;Lu H;Trivedi M;Fava M;McGrath PJ;Weissman M;Kurian BT;Adams P;Weyandt S;Toups M;Carmody T;McInnis M;Cusin C;Cooper C;Oquendo MA;Parsey RV;DeLorenzo C
通讯作者：
DeLorenzo C
影响因子：
11
作者：
Hobbs, Nicola Z.;Henley, Susie M. D.;Tabrizi, Sarah J.
通讯作者：
Tabrizi, Sarah J.
影响因子：
5.7
作者：
Derakhshan, Mishkin;Caramanos, Zografos;Collins, D. Louis
通讯作者：
Collins, D. Louis
影响因子：
10.5
作者：
Dominguez, Juan F.;Stout, Julie C.;Georgiou-Karistianis, Nellie
通讯作者：
Georgiou-Karistianis, Nellie
影响因子：
2.6
作者：
Johnson, Eileanoir B.;Rees, Elin M.;Scahill, Rachael I.
通讯作者：
Scahill, Rachael I.