Abstract
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.
Highlights
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits
The neuron reconstructions were taken from a songbird basal ganglia data set and consisted of flood-filling network (FFN)-created supervoxels25 (SVs), which were agglomerated to sets of super-SVs (SSVs)8, each corresponding to a single neuron
We explored the information content of the embedding through inspection of clusters in a 2D t-SNE26 projection (Fig. 2a) of an example cell reconstruction (Fig. 2b; colors as in Fig. 2a; see “Methods”) and by fitting a k-nearest neighbor classifier to the Neuron2vec encoding extracted from a set of neurites with cellular compartment ground truth annotations
Summary
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Multibeam scanning electron microscopes and transmission electron microscopes equipped with fast camera arrays can generate data sets exceeding 100 TB3, a development that was accompanied by substantial progress in neuron reconstruction and the automatic analysis of synapses. Multibeam scanning electron microscopes and transmission electron microscopes equipped with fast camera arrays can generate data sets exceeding 100 TB3, a development that was accompanied by substantial progress in neuron reconstruction and the automatic analysis of synapses10–13 These advances have enabled automatic morphology analyses on the neuron (fragment) scale, which were previously restricted to direct segmentation error detection or the use of manual skeletons with data-specific hand-crafted features. We present cellular morphology neural networks (CMNs), which use multi-view projections to enable the supervised and unsupervised analysis of cell fragments of arbitrary size while retaining high resolution. We identify neuronal cell types and compartments, outperforming methods with hand-crafted features based on skeleton representations on the same data, and perform high-resolution cell surface segmentation to identify dendritic spines
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