Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volume electron microscopy (EM), which entails an excruciatingly difficult process because it requires cutting tissue into many thin, fragile slices that then need to be imaged, aligned, and reconstructed. Unlike EM, hard X-ray imaging is compatible with thick tissues, eliminating the need for thin sectioning, and delivering fast acquisition, intrinsic alignment, and isotropic resolution. Unfortunately, current state-of-the-art X-ray microscopy provides much lower resolution, to the extent that segmenting membranes is very challenging. We propose an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images with enhanced membrane segmentation quality, showing its potential for developing simpler, faster, and more accurate X-ray based connectomics pipelines.
@article{arxiv.2303.00882,
title = {X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics},
author = {Yicong Li and Yaron Meirovitch and Aaron T. Kuan and Jasper S. Phelps and Alexandra Pacureanu and Wei-Chung Allen Lee and Nir Shavit and Lu Mi},
journal= {arXiv preprint arXiv:2303.00882},
year = {2023}
}
Comments
Accepted by ISBI 2023 conference. Supplementary material is available in this arXiv version