Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets

TL;DR

This paper introduces a deep learning-based method for detecting synapse locations and connectivity directions in electron microscopy data, capable of handling both dyadic and polyadic synapses across different species.

Contribution

It presents a novel algorithm that combines voxelwise signed proximity predictions with a 3D CNN for pruning, enabling simultaneous detection of synapse location and direction for diverse synapse types.

Findings

Outperforms existing methods in rodent and fruit fly brain data

Accurately detects both dyadic and polyadic synapses

Provides reliable predictions of synaptic connectivity orientation

Abstract

Synaptic connectivity detection is a critical task for neural reconstruction from Electron Microscopy (EM) data. Most of the existing algorithms for synapse detection do not identify the cleft location and direction of connectivity simultaneously. The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types. In this paper, we present an algorithm to automatically predict the location as well as the direction of both dyadic and polyadic synapses. The proposed algorithm first generates candidate synaptic connections from voxelwise predictions of signed proximity generated by a 3D U-net. A second 3D CNN then prunes the set of candidates to produce the final detection of cleft and connectivity orientation. Experimental results demonstrate that the proposed method outperforms the existing methods for determining synapses in both rodent and fruit fly brain.