Breaking Free from the Acquisition Dogma for Volume Electron Microscopy

Volume electron microscopy (VEM) enables three-dimensional (3D) visualization of thick specimens through serial sectioning and nanometer-resolution imaging. In an effort to overcome daunting challenges in subsequent data analysis, people tend to employ excessively slow imaging and high resolution, both of which significantly prolong the acquisition. Here, using authentic and synthetic VEM datasets through image stack re-registration, we demonstrate the implications of 3D oversampling in maintaining structural information fidelity against high background noise. This provides a fact-based argument for a higher priority of improving the spatial sampling rate, particularly in the most difficult axial direction, than suppressing the image noise during a VEM acquisition. To accelerate VEM acquisition by leveraging the oversampled 3D context, we explore distinct machine learning-based methods for restoring serial images that contain either low-contrast snapshots or skipped sections. On the datasets of similar acquisition consumptions, 3D context-based (volumetric) denoising outperforms state-of-the-art interpolation and 2D denoising approaches. Furthermore, the volumetric denoising models can proceed in a self-supervised manner, thereby no longer relying on specific training datasets. This work is not only instructive for planning efficient large-scale acquisition on commercial setups but also benchmarks the methodology of optimizing VEM acquisition to facilitate automated image processing.