Deep learning-based volumetric denoising enables efficient acquisition of volume electron microscopy

Volume electron microscopy (VEM) enables three-dimensional (3D) visualization of specimens through serial sectioning and nanometer-resolution imaging. To alleviate daunting difficulties in subsequent data analysis, people tend to employ excessively slow imaging and high resolution, limiting the acquisition throughput. Here, we titrated the combinations of acquisition parameters for preserving key structural information. This revealed a prior role of sufficient spatial sampling over high image contrast. To save acquisition time, we then attempted to restore serial images that contained either low-contrast snapshots or skipped sections using machine learning. Owing to constraints of cytoarchitecture rationality, 3D context-based (volumetric) denoising not only preserved more structural features but also generated fewer artifacts than other methods. Moreover, volumetric denoising lowered the requirement of imaging dwell time and thereby allowed serial block-face removal down to 20 nm because of reduced radiation damage. This work demonstrated how machine learning-based image processing enabled the optimization of VEM acquisition parameters.