Markov Random Field-based Fitting of a Subdivision-based Geometric Atlas.
Markov Random Field-based Fitting of a Subdivision-based Geometric Atlas.
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DOI：
10.1109/iccv.2011.6126541
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Kakadiaris IA
中科院分区：
文献类型：
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作者：
Kurkure U;Le YH;Paragios N;Ju T;Carson JP;Kakadiaris IA
为了比较图像之间的数据以进行空间分析，需要对多部分、复杂的解剖结构（例如大脑）进行准确标记。它可以通过拟合使用分区的高分辨率可变形网格构建的特定于对象的几何图集并用区域标签标记其每个多边形来实现。细分网格已被用来构建这样的图集，因为它们可以仅使用几个控制点来提供分区的、多分辨率的、特定于对象的网格结构的紧凑表示。然而，将基于细分网格的几何图集自动拟合到图像中的解剖结构是一个难题，并且尚未得到充分解决。在本文中，我们提出了一种基于马尔可夫随机场的新颖方法，用于将平面、多部分细分网格拟合到解剖数据。通过确定控制点的最佳位置来获得图集的最佳拟合。我们还通过构建单个图形模型来对图集变形施加姿势不变、基于地标的几何约束，从而解决地标匹配与图集拟合的问题。图集变形还受到网格几何属性和对象边界施加的附加约束的控制。我们证明了所提出的方法在分割小鼠大脑及其内部区域的基因表达图像这一难题上的潜力，这些图像表现出较大的强度和形状变异性。与手动注释和现有方法相比，我们获得了有希望的结果。
An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh’s geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.