X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: Formation of a High Flux Student Research Network (HF-SRN) as a Laboratory for Enhancing Interaction in the PoLS SRN, US-Israel Collaboration: Collaborative Research: New Tools for Extracting Neuronal Phenotypes from a Volumetric Set of Cerebral Cortex Images, G434337789, BRAIN EAGER: Functional dynamics of whole brain activity, behavior, and development from birth to adulthood, A 5-dimensional connectomics approach to the neural basis of behavior, SHF: Medium: Collaborative Research: Run-Time Support for Scalable Concurrent Programming, Bright, coherent and focused light to resolve neural circuits
OpenAlex ID: W4394665660