SynapseNet: Deep Learning for Automatic Synapse Reconstruction

Electron microscopy provides unmatched detail for studying synaptic morphology, but manual segmentation is a bottleneck. SynapseNet is a deep-learning tool that automates not only synapse reconstruction but also efficient, scalable analysis. It is trained on a large annotated room temperature EM dataset and can segment structures such as synaptic vesicles (SV), mitochondria, active zones (AZ), and compartments. With domain adaptation functionality, it also reliably segments SVs across a wide range of imaging techniques and data domains.

For more details on SynapseNet check out the software at https://github.com/computational-cell-analytics/synapse-net and the preprint at https://doi.org/10.1101/2024.12.02.626387.