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

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.

DLMIA 2017 accepted

References in corpus (1)

• Very Deep Convolutional Networks for Large-Scale Image Recognition

Cited by in corpus (5)

• Non-rigid image registration using fully convolutional networks with deep self-supervision
• Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data
• Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision
• On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
• Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy