Jeremy Maitin-Shepard

CAVE enables reproducible connectome analysis in large-scale datasets during ongoing annotation and proofreading.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

SegCLR, a self-supervised learning method, enables classification of cellular subcompartments and cell types from 3D electron microscopy data with minimal labeled data.

This paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

Flood-filling networks with local section realignment segment a forty-teravoxel whole-brain Drosophila electron microscopy volume, making circuit reconstruction an order of magnitude faster than manual tracing.

The paper presents a detailed connectome of the adult Drosophila central brain, including new methods for data analysis and biological insights into neural circuit organization.

A dense electron-microscopy reconstruction of half the fly central brain, with nearly every cell typed and queryable online, turns months of tracing partners into a database lookup.

This paper introduces semi-automated strategies combining 3D visualization and machine guidance to accelerate EM connectome reconstruction, showing improved performance over prior methods.

This paper introduces flood-filling networks for automated neuron reconstruction, achieving high precision in tracing neurites from electron microscopy data.