CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy

TL;DR

CleftNet is a novel deep learning model that enhances synaptic cleft detection in brain EM images by using feature and label augmentors, achieving top performance in the CREMI challenge.

Contribution

The paper introduces two innovative network components, the feature augmentor and label augmentor, to improve cleft detection accuracy in EM images.

Findings

Ranks #1 on CREMI online challenge.

Outperforms baseline methods significantly.

Effective in both online and offline tasks.

Abstract

Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by photoing EM images with high resolution and fine details. Machine learning approaches have been employed to automatically predict synaptic clefts from EM images. In this work, we propose a novel and augmented deep learning model, known as CleftNet, for improving synaptic cleft detection from brain EM images. We first propose two novel network components, known as the feature augmentor and the label augmentor, for augmenting features and labels to improve cleft representations. The feature augmentor can fuse global information from inputs and learn common morphological patterns in clefts, leading to augmented cleft features. In addition, it can generate outputs with varying dimensions, making it flexible to be integrated in any deep network. The proposed label augmentor augments the label of each voxel from a value to a vector, which contains both the segmentation label and boundary label. This allows the network to learn important shape information and to produce more informative cleft representations. Based on the proposed feature augmentor and label augmentor, We build the CleftNet as a U-Net like network. The effectiveness of our methods is evaluated on both online and offline tasks. Our CleftNet currently ranks \#1 on the online task of the CREMI open challenge. In addition, both quantitative and qualitative results in the offline tasks show that our method outperforms the baseline approaches significantly.