Learning cellular morphology with neural networks
Learning cellular morphology with neural networks
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DOI：
10.1038/s41467-019-10836-3
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发表时间：
2019-06-21
影响因子：
16.6
通讯作者：
Kornfeld, Joergen
中科院分区：
文献类型：
--
作者：
Schubert, Philipp J.;Dorkenwald, Sven;Kornfeld, Joergen
脑组织的体积电子显微镜数据集的重建和注释是具有挑战性的，但可以揭示有关神经元回路的宝贵信息。最近在自动神经元重建以及突触的自动检测方面取得了重大进展。然而，自动化的纳米分辨率重建的形态学分析的方法较少建立，尽管可能的应用的多样性。在这里，我们介绍细胞形态神经网络（CMNs），基于多视图投影采样自动重建的细胞碎片的任意大小和形状。使用无监督训练，我们推断神经元重建的形态嵌入（Neuron2vec），并训练CMN以在监督分类范例中识别神经胶质细胞，然后用于解决神经元重建错误。最后，我们证明了CMNs可用于识别亚细胞区室和神经元重建的细胞类型。
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.