SyConn2: dense synaptic connectivity inference for volume electron microscopy

Multi-layered maps of neuropil with segmentation-guided contrastive learning

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

High-Speed Automated Reconstruction of Drosophila Larval Brain from Volumetric EM Data

To uncover the relationship between neural activity and behavior, it is essential to reconstruct neural circuits. However, methods typically used for neuron reconstruction from volumetric electron microscopy (EM) dataset are often time-consuming and require extensive manual proofreading, making it difficult to reproduce in a typical laboratory setting. To address this challenge, we have developed a set of acceleration techniques that build upon the Flood Filling Network (FFN), significantly reducing the time required for this task. These techniques can be easily adapted to other similar datasets and laboratory settings. To validate our approach, we tested our pipeline on a dataset of Drosophila larval brain serial section EM images at synaptic-resolution level. Our results demonstrate that our pipeline significantly reduces the inference time compared to the FFN baseline method and greatly reduces the time required for reconstructing the 3D morphology of neurons.