Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.
Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.
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DOI：
10.1016/j.media.2012.06.004
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发表时间：
2012-08
影响因子：
10.9
通讯作者：
Kleinfeld, David
中科院分区：
文献类型：
--
作者：
Kaufhold, John P.;Tsai, Philbert S.;Blinder, Pablo;Kleinfeld, David
组织脉管系统图是对血液和大脑细胞之间的气体和营养交换进行建模的基本要求。这样的图是从解剖数据的矢量化表示导出的，提供了所有血管作为顶点和片段的图，并且可以包括非血管成分的位置，例如神经元和神经胶质细胞体。然而，矢量化数据集通常包含错误的间隙，虚假的端点和虚假的合并链。当前校正这种缺陷的方法仅解决连接间隙的问题，并且还需要在高维算法中手动调整参数。为了解决这些缺点，我们引入了一种有监督的机器学习方法，该方法（1）通过“学习阈值松弛”连接血管间隙;（2）通过“学习消除删除候选链”去除虚假片段;以及（3）通过“一致性学习”在学习的血管图校正的联合空间中实施一致性。人类操作员只需要标记他们在训练集中识别的单个对象，并且不需要调整参数。监督学习过程检查所考虑的每个血管段的邻域中的特征的几何形状和拓扑结构。我们证明了这些方法对四组微血管数据的有效性，每组数据具有> 8003体素，通过最先进的图像分割技术获得小鼠组织的所有光学组织学和矢量化。通过统计验证的抽样和精确召回曲线分析，我们发现使用袋装提升决策树学习将阈值松弛的等误错误率降低了5%至21%，并将链消除性能降低了18%至57%。我们对数据集的泛化性能进行了基准测试;虽然数据集之间的改进有所不同，但学习总是会导致错误率的有效降低。总的来说，学习显示出超过一半的总错误率，因此，人工花费的时间手动纠正这种矢量化。
A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by “learned threshold relaxation”; (2) removes spurious segments by “learning to eliminate deletion candidate strands”; and (3) enforces consistency in the joint space of learned vascular graph corrections through “consistency learning.” Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with > 8003 voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5 to 21 % and strand elimination performance by 18 to 57 %. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations.
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影响因子：
3
作者：
Bjornsson, Christopher S.;Lin, Gang;Roysam, Badrinath
通讯作者：
Roysam, Badrinath
影响因子：
10.9
作者：
Friman, Ola;Hindennach, Milo;Peitgen, Heinz-Otto
通讯作者：
Peitgen, Heinz-Otto
影响因子：
5.3
作者：
Devor, Anna;Tian, Peifang;Dale, Anders M.
通讯作者：
Dale, Anders M.
影响因子：
1.1
作者：
Freund, Y;Schapire, RE
通讯作者：
Schapire, RE
影响因子：
7.5
作者：
Breiman, L
通讯作者：
Breiman, L