3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries
3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries
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DOI：
10.1016/j.media.2011.11.004
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发表时间：
2012-05-01
影响因子：
10.9
通讯作者：
Hamprecht, Fred A.
中科院分区：
文献类型：
--
作者：
Andres, Bjoern;Koethe, Ullrich;Hamprecht, Fred A.
利用连续切片电子显微镜对大体积神经细胞图像进行分割是实现神经回路三维重建的重要步骤。手头的数据提供的唯一线索是无法区分的物体之间的边界。这种不可区分性，加上边界在某些地方变得非常薄或模糊，使得基于区域的分割方法的大量工作不适用。另一方面，纯粹利用局部证据的基于边界的方法不能达到应用程序领域所要求的极高精度，因为应用程序领域不能容忍由错误的局部决策引起的全局拓扑错误。因此，我们提出了一种超体素合并方法，该方法通过在超体素之间的所有面上提出并近似解决联合组合优化问题，以非局部方式得出其决策。使用超体素可以提取具有表现力的几何特征。这些被图形模型中的高阶电位使用，该模型通过在金标准上的自动训练来吸收有关神经表面几何形状的知识。这种改进的范围在来自兔子视网膜内网状层的75亿体素的基准数据集E1088 （Helmstaedter et al., 2011）上得到了证明。我们提供了用于标注、几何提取、训练和推理的c++源代码。(C) 2011 Elsevier B.V.版权所有
The segmentation of large volume images of neuropil acquired by serial sectioning electron microscopy is an important step toward the 3D reconstruction of neural circuits. The only cue provided by the data at hand is boundaries between otherwise indistinguishable objects. This indistinguishability, combined with the boundaries becoming very thin or faint in places, makes the large body of work on region-based segmentation methods inapplicable. On the other hand, boundary-based methods that exploit purely local evidence do not reach the extremely high accuracy required by the application domain that cannot tolerate the global topological errors arising from false local decisions. As a consequence, we propose a supervoxel merging method that arrives at its decisions in a non-local fashion, by posing and approximately solving a joint combinatorial optimization problem over all faces between supervoxels. The use of supervoxels allows the extraction of expressive geometric features. These are used by the higher-order potentials in a graphical model that assimilate knowledge about the geometry of neural surfaces by automated training on a gold standard. The scope of this improvement is demonstrated on the benchmark dataset E1088 (Helmstaedter et al., 2011) of 7.5 billion voxels from the inner plexiform layer of rabbit retina. We provide C++ source code for annotation, geometry extraction, training and inference. (C) 2011 Elsevier B.V. All rights reserved.