ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features

The alignment of serial-section electron microscopy (ssEM) images is critical for eorts in neuroscience that seek to reconstruct neu- ronal circuits. However, each ssEM plane contains densely packed struc-tures that vary from one section to the next, which makes matching fea-tures 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 convo-lutional 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

Weakly supervised learning in deformable em image registration using slice interpolation

Alignment of large-scale serial-section electron microscopy (ssEM) images is crucial for successful analysis in nano-scale connectomics. Despite various image registration algorithms proposed in the past, large-scale ssEM alignment remains challenging due to the size and complex nature of the data. Recently, the application of unsupervised machine learning in medical image registration has shown promise in efforts to replace an expensive numerical computation process with a once-deployed feed-forward neural network. However, the anisotropy in most ssEM data makes it difficult to directly adopt such learning-based methods for the registration of these images. Here, we propose a novel deformable image registration approach based on weakly supervised learning that can be applied to registering ssEM images at scale. The proposed method leverages slice interpolation to improve registration between images with sudden and large structural changes. In addition, the proposed method only requires roughly aligned data for training the interpolation network while the deformation network can be trained in an unsupervised fashion. We demonstrate the efficacy of the method on real ssEM datasets