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@ARTICLE{Siggel:613831,
      author       = {Siggel, Marc and Jensen, Rasmus K. and Maurer, Valentin J.
                      and Mahamid, Julia and Kosinski, Jan},
      title        = {{C}olab{S}eg: {A}n interactive tool for editing,
                      processing, and visualizing membrane segmentations from
                      cryo-{ET} data},
      journal      = {Journal of structural biology},
      volume       = {216},
      number       = {2},
      issn         = {1047-8477},
      address      = {San Diego, Calif.},
      publisher    = {Elsevier},
      reportid     = {PUBDB-2024-05637},
      pages        = {108067},
      year         = {2024},
      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.},
      cin          = {CSSB-EMBL-JK},
      ddc          = {540},
      cid          = {I:(DE-H253)CSSB-EMBL-JK-20210701},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38367824},
      UT           = {WOS:001205861300001},
      doi          = {10.1016/j.jsb.2024.108067},
      url          = {https://bib-pubdb1.desy.de/record/613831},
}