ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features

TL;DR

This paper presents ssEMnet, a deep learning model that combines a spatial transformer and autoencoder to improve the accuracy and robustness of serial-section electron microscopy image registration with less user intervention.

Contribution

Introduction of a novel deep network combining spatial transformer and autoencoder for unsupervised ssEM image registration, enhancing accuracy and robustness.

Findings

Improved registration accuracy across multiple datasets.

Reduced user intervention compared to traditional methods.

Robustness to densely packed structures in ssEM images.

Abstract

The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across several datasets.