Learning cellular morphology with neural networks

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 their diverse application possibilities. 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 inferred morphology embeddings ("Neuron2vec") of neuron reconstructions and trained CMNs to identify glia cells in a supervised classification paradigm which was 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.

Organizing Principles of Astrocytic Nanoarchitecture in the Mouse Cerebral Cortex

“…Instead, multiple rounds of manual segmentation and detailed quality control by experienced astrocyte biologists were required to find complete sets of connected astrocytic branches in our reference volumes (see Methods). Indeed, segmentation of nanoscopic branches of astrocytes are an acknowledged challenge in the field (Rusakov, 2015), and in fact, special attention must be paid to removing segmenting errors caused by the presence of astrocytes in current state-of-the-art automated segmentation approaches (Januszewski et al, 2018;Schubert et al, 2019). While crowdsourcing astrocytic segmentation followed by stringent quality control may be a viable alternative (Arganda-Carreras et al, 2015), astrocyte-specific auto-segmentation strategies are likely needed to reconstruct fine astrocytic nanostructure for complete cells and fully integrate astrocytes into the pursuit of saturated reconstructions and connectomes.…”

Virtual Fly Brain—An interactive atlas of the Drosophila nervous system

“…In the near future, VFB will ingest multiple large connectomics datasets with variable coverage and accuracy of neuron type annotation. BLAST-like algorithms, in the short-term NBLAST for morphology, but longer term supplemented by CBLAST (Scheffer et al, 2020) for connectivity and potentially methods that use subcellular features (Schubert et al, 2019;Zinchenko et al, 2022), will be critical to help users to interpret this data by facilitating prediction and assignment of neuron types. For example, a user finding paths between untyped neurons from FlyWire using our circuit browsing tool will be able to use NBLAST to find predicted types for neurons in the circuit, where these exist in other reference data sets.…”