  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.
