3D Synaptic Cleft Detection of EM Images with Multi-Scale Deep Learning Architectures

Abstract

With recent advances in electron microscopic (EM) imaging, researchers now have unprecedented access to high resolution image data of neural circuits. Automatic reconstruction of these circuits has shown be vital to the field of connectomics, as manual annotation of EM images is far too time consuming. Current state of the art methods use deep convolutional neural networks (DCNNs) for automatic 3D neuron segmentation reconstruction. In this paper, we leverage current works for the task of synapse detection in adult fly brain, building off of the multi-scale 3D UNet architecture used in [9] for neuron segmentation of EM data. In addition, we demonstrate the effects of gradient masking and multi-task learning with the belief that their application helps remedy challenges specific to the synapse detection problem. We show that the use of multi-task learning, with neuron segmentation as the auxiliary task, can achieve better precision and recall for synapse detection than independent task learning. We argue that reason for improvements result from a shared representation in lower dimensional structure.