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

arXiv: https://arxiv.org/abs/1707.07833 · HTML: https://mlsanity.com/paper/1707.07833/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024)
- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [CartiMorph: a framework for automated knee articular cartilage morphometrics](https://mlsanity.com/paper/2308.01981/) (2023)
- [A Transformer-based Network for Deformable Medical Image Registration](https://mlsanity.com/paper/2202.12104/) (2022)
- [Towards Establishing Dense Correspondence on Multiview Coronary Angiography: From Point-to-Point to Curve-to-Curve Query Matching](https://mlsanity.com/paper/2312.11593/) (2023)
- [Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data](https://mlsanity.com/paper/2408.14270/) (2024)
- [A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing](https://mlsanity.com/paper/2305.13855/) (2023)

## Abstract (arXiv)

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.