2024Siggel ColabSeg

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Revision as of 06:28, 13 August 2024 by WikiSysop (talk | contribs) (Created page with "== Citation == Siggel, Marc / Jensen, Rasmus K. / Maurer, Valentin J. / Mahamid, Julia / Kosinski, Jan. ColabSeg: An interactive tool for editing, processing, and visualizing membrane segmentations from cryo-ET data. 2024. J. Structural Biology, Vol. 216, No. 2, p. 108067 == Abstract == 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...")
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Citation

Siggel, Marc / Jensen, Rasmus K. / Maurer, Valentin J. / Mahamid, Julia / Kosinski, Jan. ColabSeg: An interactive tool for editing, processing, and visualizing membrane segmentations from cryo-ET data. 2024. J. Structural Biology, Vol. 216, No. 2, p. 108067

Abstract

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.

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https://www.sciencedirect.com/science/article/pii/S1047847724000078

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