vEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy.

Background: As software and hardware have developed, so has the scale of research into volume electron microscopy (vEM), leading to ever-increasing resolution. Usually, data collection is followed by image stitching: the same area is subjected to high-resolution imaging with a certain overlap, and then the images are stitched together to achieve ultrastructure with large scale and high resolution simultaneously. However, there is currently no perfect method for image stitching, especially when the global feature distribution of the sample is uneven and the feature points of the overlap area cannot be matched accurately, which results in ghosting of the fusion area.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities.

Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network.

Motivation: Serial-section electron microscopy (ssEM) is a powerful technique for cellular visualization, especially for large-scale specimens. Limited by the field of view, a megapixel image of whole-specimen is regularly captured by stitching several overlapping images. However, suffering from distortion by manual operations, lens distortion or electron impact, simple rigid transformations are not adequate for perfect mosaic generation.