Detection of neuron membranes in electron microscopy images using a serial neural network architecture.
Detection of neuron membranes in electron microscopy images using a serial neural network architecture.
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DOI：
10.1016/j.media.2010.06.002
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发表时间：
2010-12
影响因子：
10.9
通讯作者：
Tasdizen, Tolga
中科院分区：
文献类型：
--
作者：
Jurrus, Elizabeth;Paiva, Antonio R. C.;Watanabe, Shigeki;Anderson, James R.;Jones, Bryan W.;Whitaker, Ross T.;Jorgensen, Erik M.;Marc, Robert E.;Tasdizen, Tolga
关键词：
通过连接体研究神经系统是神经科学中一个具有挑战性的问题。为了实现这一目标，神经生物学家正在获取大型电子显微镜数据集。然而，这些数据集的剪切体积使得手动分析不可行。因此，需要自动图像分析方法来从这些非常大的图像集合重建连接体。这些图像中神经元的分割是重建管道的一个重要步骤，由于噪声、各向异性形状和亮度以及混淆结构的存在而具有挑战性。在本文中描述的方法使用了一系列的人工神经网络（ANN）的框架相结合的特征向量，该特征向量是由在模板邻域采样的图像强度。几个人工神经网络串联应用，允许每个人工神经网络使用前一个网络提供的分类上下文来提高检测精度。我们开发了串行人工神经网络的方法，并表明学习的上下文确实比传统的人工神经网络提高了检测。我们还证明了优于以前的膜检测方法。这些结果是迈向连接体重建自动化系统的重要一步。
Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome.
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影响因子：
5.3
作者：
DeBello, WM;Feldman, DE;Knudsen, EI
通讯作者：
Knudsen, EI
影响因子：
64.8
作者：
JIN, YS;HOSKINS, R;HORVITZ, HR
通讯作者：
HORVITZ, HR
影响因子：
19.5
作者：
Leung, T;Malik, J
通讯作者：
Malik, J
影响因子：
7.8
作者：
HORNIK, K
通讯作者：
HORNIK, K
DOI：
10.1109/tpami.2004.1262185
发表时间：
2004-02-01
影响因子：
23.6
作者：
Fowlkes, C;Belongie, S;Malik, J
通讯作者：
Malik, J