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首页> 外文期刊>Neuroinformatics >Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites
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Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

机译：识别非均匀神经元图像中的弱信号，用于稀疏分布神经癖的大规模追踪

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Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.
机译：追踪神经癖构成神经元形态重建的核心，是神经元电路映射的关键步骤。现代光学成像技术允许观察脑区几乎完全的小鼠神经元形态甚至整个大脑。然而，神经元的高级自动化重建，即，具有几种手动编辑的重建需要歧视来自非均匀背景的弱前景点。我们构建了一种识别模型，其中总结了由神经元图像进行的经验观察，以便设计用​​于对前景和背景进行分类的特征向量，并且使用支持向量机（SVM）来学习这些特征向量。我们将该构建的SVM分类器嵌入到先前开发的工具Sparsetracer中，以获得Sparsetracer学习的特征向量（ST-LFV）。 ST-LFV可以通过广泛使用的光学显微镜技术和双光子显微镜等广泛使用的光学光学技术成像的数据集中的弱信号（对比度对阴噪音<1.5）追踪稀疏的信号（对比度对比度<1.5）。此外，从不同的脑区提取12个子嵌段。平均召回和精密率分别为99％和97％。这些结果表明，ST-LFV非常适合具有不同图像特性的弱信号识别。我们还应用ST-LFV以追踪来自神经疾病的图像的远程神经脉，但在某些情况下，它们的图像强度薄弱。在追踪这种远程神经态时，需要手动编辑一次，以获得相当于地面真理的结果，而Sparsetracer所需的20次手动编辑相比。这种改进的自动重建水平表明，ST-LFV具有迅速地以大规模迅速地重建稀疏的分布神经元。

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