A Bayesian model of shape and appearance for subcortical brain segmentation.
A Bayesian model of shape and appearance for subcortical brain segmentation.
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DOI：
10.1016/j.neuroimage.2011.02.046
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发表时间：
2011-06-01
期刊：
影响因子：
5.7
通讯作者：
Jenkinson, Mark
中科院分区：
文献类型：
--
作者：
Patenaude, Brian;Smith, Stephen M.;Kennedy, David N.;Jenkinson, Mark
人脑MR图像中皮层下结构的自动分割是一项重要但困难的任务，因为对比度差且强度可变。在典型结构边界的许多地方缺乏清晰、明确的强度特征，因此需要额外的信息来实现成功的分割。本文提出了一种使用人工标记图像数据来提供解剖训练信息的方法。它利用了活动形状和外观模型的原理，但将它们置于贝叶斯框架中，允许形状和强度之间的概率关系得到充分利用。该模型使用336张手动标记的t1加权MR图像对15种不同的皮质下结构进行了训练。使用贝叶斯方法，条件概率可以轻松有效地计算，避免了病态协方差矩阵的技术问题，即使具有弱先验，并且消除了标准活动外观模型中需要拟合额外的经验尺度参数的需要。此外，边界顶点位置的差异提供了一种直接的、纯粹的局部测量方法，可以测量群体之间结构的几何变化，与基于体素的形态测量不同，它不依赖于组织分类方法或任意平滑。本文提出了一种全自动分割方法，并对其进行了定量评估（使用对336张训练图像进行留一测试）和定性评估（使用涉及阿尔茨海默病的独立临床数据集）。使用这种方法可以获得0.7和0.9之间的中位数骰子重叠，这与其他自动化方法相当或更好。该方法的一个实现称为FIRST，目前与可免费获得的FSL包一起发布。
Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer’s disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.
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