Cortical sulci recognition and spatial normalization
Cortical sulci recognition and spatial normalization
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DOI：
10.1016/j.media.2011.02.008
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发表时间：
2011-08-01
影响因子：
10.9
通讯作者：
Mangin, Jean-Francois
中科院分区：
文献类型：
--
作者：
Perrot, Matthieu;Riviere, Denis;Mangin, Jean-Francois
大脑图谱技术将个体之间相似的解剖信息配对。在这种情况下，空间归一化主要用于减少受试者间差异以提高可比性。这些技术可能受益于有助于驱动配准的解剖学识别标志。自动标记、分类或分割技术提供了这样的标签。这些方法中的大多数强烈依赖于归一化，就像归一化依赖于地标准确性一样。我们在本文中提出了一个连贯的贝叶斯框架，基于概率图集（SPAM 模型的混合：统计概率解剖图）自动识别每个半球大约 60 个脑沟标签，同时估计归一化参数。这样，标记方法也无需额外的计算成本即可提供新的脑沟结构的自动约束配准。我们的研究仅限于全局仿射和分段仿射配准。就脑沟对齐而言，建议的全局仿射方法明显优于基于标准仿射强度的归一化技术。此外，通过结合全局和局部联合标记，最终获得了 86% 的平均识别率，并且标记后验概率更加可靠。自 BrainVISA 软件平台 3.2.1 版本发布以来，本文描述的不同方法已被集成（Riviere 等人，2009）。 (C) 2011 Elsevier B.V. 保留所有权利。
Brain mapping techniques pair similar anatomical information across individuals. In this context, spatial normalization is mainly used to reduce inter-subject differences to improve comparisons. These techniques may benefit from anatomically identified landmarks useful to drive the registration. Automatic labeling, classification or segmentation techniques provide such labels. Most of these approaches depend strongly on normalization, as much as normalization depends on landmark accuracy. We propose in this paper a coherent Bayesian framework to automatically identify approximately 60 sulcal labels per hemisphere based on a probabilistic atlas (a mixture of SPAM models: Statistical Probabilistic Anatomy Map) estimating simultaneously normalization parameters. This way, the labelization method provides also with no extra computational costs a new automatically constrained registration of sulcal structures. We have limited our study to global affine and piecewise affine registration. The suggested global affine approach outperforms significantly standard affine intensity-based normalization techniques in term of sulci alignments. Further, by combining global and local joint labeling, a final mean recognition rate of 86% has been obtained with much more reliable labeling posterior probabilities. The different methods described in this paper have been integrated since the release version 3.2.1 of the BrainVISA software platform (Riviere et al., 2009). (C) 2011 Elsevier B.V. All rights reserved.