Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
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DOI：
10.1016/j.neuroimage.2008.12.037
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发表时间：
2009-07-01
期刊：
影响因子：
5.7
通讯作者：
Parsey RV
中科院分区：
文献类型：
--
作者：
Klein A;Andersson J;Ardekani BA;Ashburner J;Avants B;Chiang MC;Christensen GE;Collins DL;Gee J;Hellier P;Song JH;Jenkinson M;Lepage C;Rueckert D;Thompson P;Vercauteren T;Woods RP;Mann JJ;Parsey RV
所有使用脑成像的神经科学领域都需要将其结果与解剖区域进行交流。特别是，功能和生理数据的比较形态测量和组分析需要大脑的配准，以建立跨大脑结构的对应关系。众所周知，一个大脑与另一个大脑的线性配准不足以对齐大脑结构，因此出现了许多算法来非线性地将大脑彼此配准。这项研究是有史以来最大的非线性变形算法应用于脑图像配准的评估。来自世界各地的实验室的14种算法使用8种不同的误差测量进行评估。80个手动标记的大脑之间的45，000多个配准通过算法进行，包括：AIR，ANIMAL，ART，双态Demons，FNIRT，IRTK，JRD-fluid，罗密欧，SICLE，SyN和四种不同的SPM 5算法（“SPM 2型”和常规归一化，统一分割和DARTEL算法）。所有这些配准之前，使用FLIRT在相同的图像对之间进行线性配准。本研究最重要的发现之一是，对比的配准方法的相对性能似乎几乎不受试者人群、标签方案和重叠测量类型的影响。这一点很重要，因为它表明这些发现可推广到使用不同标记方案标记或评价的新受试者人群。此外，我们排名的14种方法，根据三个完全独立的分析（排列检验，单因素方差分析检验，和无差异区排名），并得出三个几乎相同的顶级排名的方法。根据重叠和距离测量，ART、SyN、IRTK和SPM的DARTEL THERM给出了最好的结果，ART和SyN在受试者和标签集之间提供了最一致的高准确性。更新将在http://www.mindboggle.info/papers/网站上发布。
All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms (“SPM2-type” and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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影响因子：
1.3
作者：
COLLINS, DL;NEELIN, P;EVANS, AC
通讯作者：
EVANS, AC
影响因子：
--
作者：
BECHHOFER, RE
通讯作者：
BECHHOFER, RE
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
DOI：
10.1007/11566489_43
发表时间：
2005-01-01
期刊：
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2005, PT 2
影响因子：
--
作者：
Clouchoux, C;Coulon, O;Régis, J
通讯作者：
Régis, J
影响因子：
4.8
作者：
Collins, DL;Holmes, CJ;Evans, AC
通讯作者：
Evans, AC