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Updated: May 31, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
Edged watershed segmentation: a semi-interactive algorithm for segmentation of low-resolution maps from electron
Lindsay A Baker1, John L Rubinstein
1The Hospital for Sick Children Research Institute, 555 University Avenue, Toronto, Ontario, Canada. la.baker@utoronto.ca
Abstract:
Electron cryomicroscopy (cryo-EM) allows for the structural analysis of large protein complexes that may be difficult to study by other means. Frequently, maps of complexes from cryo-EM are obtained at resolutions between 10 and 25Å. To aid in the interpretation of these medium- to low-resolution maps, they may be subdivided into three-dimensional segments representing subunits or subcomplexes. This division is often accomplished using a manual segmentation approach. While extremely useful, manual segmentation is subjective. We have developed a novel semi-interactive segmentation algorithm that can incorporate prior knowledge of subunit composition or structure without biasing the boundaries between subunits or subcomplexes. This algorithm has been characterized with experimental and simulated cryo-EM density maps at resolutions between 10 and 25Å.

