X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
This work addresses the bottleneck of slow and difficult electron microscopy in brain imaging for connectomics researchers, offering a potential incremental improvement by combining X-ray speed with enhanced resolution.
The paper tackles the problem of low-resolution X-ray microscopy in connectomics by proposing an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images, resulting in enhanced membrane segmentation quality for simpler, faster, and more accurate pipelines.
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.