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    Center for Computational and Theoretical Biology

    Summary

    3D electron microscopy provides detailed ultrastuctural information, but manual segmentation of such images is still the de-facto standard. We develop methods to automatically extract quantitative stuctural information from such dense, large-scale image volumes from electron microscopy.

    Details

    Targeted segmentation

    Using classical watershed-based segmentation and object features, we develop tools to automatically extract well-defined structures from electron microscopy images, e.g. presynaptic vesicles from electron tomograms.

    Trainable methods and deep learning

    We apply trainable pixel classifiers and convolutional networks to explore large 3D EM volumes and to determine quantitative structural detail.

    Publications

    [ 2017 ]

    2017 [ to top ]

    • FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms. Kaltdorf, Kristin Verena; Schulze, Katja; Helmprobst, Frederik; Kollmannsberger, Philip; Dandekar, Thomas; Stigloher, Christian in PLOS Computational Biology (2017). 13(1) 1-21.
       
    Contact

    Center for Computational and Theoretical Biology
    Gebäude 32
    Campus Hubland Nord
    97074 Würzburg

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    Hubland Nord, Geb. 32