2024/02/15 by Marc Siggel, Rasmus K. Jensen, Valentin J. Maurer +4 · 15 citations
Biochemistry, Genetics and Molecular Biology · #Advanced Electron Microscopy Techniques and Applications #Advanced Fluorescence Microscopy Techniques #Artificial intelligence #Computer graphics (images) #Computer science #Protein Structure and Dynamics #Visualization
paper · doi:10.1016/j.jsb.2024.108067
published in Journal of Structural Biology 216(2), 108067 (Elsevier BV)
openalex publication_date 2024/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cellular cryo-electron tomography (cryo-ET) has emerged as a key method to unravel the spatial and structural complexity of cells in their near-native state at unprecedented molecular resolution. To enable quantitative analysis of the complex shapes and morphologies of lipid membranes, the noisy three-dimensional (3D) volumes must be segmented. Despite recent advances, this task often requires considerable user intervention to curate the resulting segmentations. Here, we present ColabSeg, a Python-based tool for processing, visualizing, editing, and fitting membrane segmentations from cryo-ET data for downstream analysis. ColabSeg makes many well-established algorithms for point-cloud processing easily available to the broad community of structural biologists for applications in cryo-ET through its graphical user interface (GUI). We demonstrate the usefulness of the tool with a range of use cases and biological examples. Finally, for a large Mycoplasma pneumoniae dataset of 50 tomograms, we show how ColabSeg enables high-throughput membrane segmentation, which can be used as valuable training data for fully automated convolutional neural network (CNN)-based segmentation.