Automated Identification of Filaments in Cryoelectron Microscopy Images

Since the foundation for the three-dimensional image reconstruction of helical objects from electron micrographs was laid more than 30 years ago, there have been sustained developments in specimen preparation, data acquisition, image analysis, and interpretation of results. However, the boxing of filaments in large numbers of images--one of the critical steps toward the reconstruction at high resolution--is still constrained by manual processing even though interactive interfaces have been built to aid the tedious and sometimes inaccurate boxing process. This article describes an accurate approach for automated detection of filamentous structures in low-contrast images acquired in defocus pairs using cryoelectron microscopy. The performance of the approach has been evaluated across various magnifications and at a series of defocus values using tobacco mosaic virus (TMV) preserved in vitreous ice as a test specimen. By integrating the proposed approach into our automated data acquisition and reconstruction system, we are now able to generate a three-dimensional map of TMV to approximately 10-A resolution within 24 h of inserting the specimen grid into the microscope.