Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )最新文献

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A Method for Automated Cortical Surface Registration and Labeling. 一种皮质表面自动配准与标记方法。

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )

Pub Date : 2012-07-01 DOI: 10.1007/978-3-642-31340-0_19

Anand A Joshi, David W Shattuck, Richard M Leahy

Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.

人脑MRI解剖特征的登记和描绘在脑发育和疾病的研究中起着重要的作用。准确、自动和计算效率高的皮质表面配准和基于表面的地标的描绘，包括感兴趣区域(roi)和沟曲线(sulci)，仍然是具有挑战性的问题，因为这些特征的形状在人群中存在很大差异。我们提出了一种快速准确的脑图像配准、标记和脑沟描绘方法。本文提出的新方法采用多分辨率、基于曲率的方法将受试者脑表面模型配准到描绘的图谱表面模型;然后将地图集roi和脑沟曲线映射到受试者的大脑表面。然后使用皮质表面的测地线曲率流来细化沟曲线的位置，并进一步标记边界，使它们更紧密地遵循真正的沟底。在皮质表面上使用基于水平集的方法来制定流，该方法将曲线表示为零水平集。我们还结合了基于曲率的加权，将曲线驱动到皮质褶皱的沟谷底部。最后，我们通过将自动绘制的沟曲线集与相应的手动绘制的沟曲线集进行比较来验证我们的新方法。结果表明，该方法能够准确地找到这些地标。

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引用次数: 52

Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty. 用于量化和可视化配准不确定性的空间置信区域。

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )

Pub Date : 2012-01-01 DOI: 10.1007/978-3-642-31340-0_13

Takanori Watanabe, Clayton Scott

For image registration to be applicable in a clinical setting, it is important to know the degree of uncertainty in the returned point-correspondences. In this paper, we propose a data-driven method that allows one to visualize and quantify the registration uncertainty through spatially adaptive confidence regions. The method applies to various parametric deformation models and to any choice of the similarity criterion. We adopt the B-spline model and the negative sum of squared differences for concreteness. At the heart of the proposed method is a novel shrinkage-based estimate of the distribution on deformation parameters. We present some empirical evaluations of the method in 2-D using images of the lung and liver, and the method generalizes to 3-D.

为了使图像配准适用于临床环境，重要的是要知道返回点对应的不确定性程度。在本文中，我们提出了一种数据驱动的方法，允许人们通过空间自适应置信区域可视化和量化配准不确定性。该方法适用于各种参数变形模型和任意相似准则的选择。我们采用b样条模型和负的差的平方和的具体。该方法的核心是一种新的基于收缩的变形参数分布估计。我们使用肺和肝脏的图像对该方法进行了一些二维的经验评估，并将该方法推广到三维。

引用次数: 9

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