Synaptic partner prediction from point annotations in insect brains

TL;DR

This paper introduces a 3D U-Net based method for predicting synaptic partners in insect brain electron microscopy data, using point annotations to improve accuracy and reduce errors in wiring diagram reconstruction.

Contribution

It presents a novel approach that formulates synaptic partner prediction as a classification of voxel pairs, leveraging point annotations for training.

Findings

Achieved 3% fewer errors than previous methods on the MICCAI 2016 CREMI challenge.

Demonstrated that point annotations can effectively train synaptic partner prediction models.

Improved accuracy in identifying pre- and postsynaptic voxel pairs in insect brain data.

Abstract

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method.