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    <title language="eng">Sperm-specific meiotic chromosome segregation in C. elegans</title>
    <parentTitle language="eng">eLife</parentTitle>
    <identifier type="doi">10.7554/eLife.50988</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2020-03-08</enrichment>
    <author>Gunar Fabig</author>
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    <author>Robert Kiewisz</author>
    <author>Norbert Lindow</author>
    <author>James A. Powers</author>
    <author>Vanessa Cota</author>
    <author>Luis J. Quintanilla</author>
    <author>Jan Brugués</author>
    <author>Steffen Prohaska</author>
    <author>Diana S. Chu</author>
    <author>Thomas Müller-Reichert</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
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    <collection role="persons" number="norbert.lindow">Lindow, Norbert</collection>
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  <doc>
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    <completedDate>2020-05-28</completedDate>
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    <title language="eng">Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography</title>
    <abstract language="eng">We present a software-assisted workflow for the alignment and matching of filamentous structures across a 3D stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite the problems related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2020.05.28.120899</identifier>
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    <enrichment key="AcceptedDate">2020-05-28</enrichment>
    <author>Norbert Lindow</author>
    <submitter>Daniel Baum</submitter>
    <author>Florian Brünig</author>
    <author>Vincent J. Dercksen</author>
    <author>Gunar Fabig</author>
    <author>Robert Kiewisz</author>
    <author>Stefanie Redemann</author>
    <author>Thomas Müller-Reichert</author>
    <author>Steffen Prohaska</author>
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  <doc>
    <id>7373</id>
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    <language>eng</language>
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    <title language="eng">Semi-automatic Stitching of Serial Section Image Stacks with Filamentous Structures</title>
    <abstract language="eng">In this paper, we present a software-assisted workflow for the alignment and matching of filamentous structures across a stack of 3D serial image sections. This is achieved by a combination of automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. This is supported by a quality assessment that visualizes regions that have been already inspected and, thus, allows a trade-off between quality and manual labor.&#13;
The software tool was developed in collaboration with biologists who investigate microtubule-based spindles during cell division. To quantitatively understand the structural organization of such spindles, a 3D reconstruction of the numerous microtubules is essential. Each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The sections then need to be stitched, i.e. non-rigidly aligned; and the microtubules need to be traced in each section and connected across section boundaries. Experiments led to the conclusion that automatic methods for stitching alone provide only an incomplete solution to practical analysis needs. Automatic methods may fail due to large physical distortions, a low signal-to-noise ratio of the images, or other unexpected experimental difficulties. In such situations, semi-automatic validation and correction is required to rescue as much information as possible to derive biologically meaningful results despite of some errors related to data collection.&#13;
Since the correct stitching is visually not obvious due to the number of microtubules (up to 30k) and their dense spatial arrangement, these are difficult tasks. Furthermore, a naive inspection of each microtubule is too time consuming. In addition, interactive visualization is hampered by the size of the image data (up to 100 GB). Based on the requirements of our collaborators, we present a practical solution for the semi-automatic stitching of serial section image stacks with filamentous structures.</abstract>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Norbert Lindow</author>
    <submitter>Norbert Lindow</submitter>
    <author>Florian Brünig</author>
    <author>Vincent J. Dercksen</author>
    <author>Gunar Fabig</author>
    <author>Robert Kiewisz</author>
    <author>Stefanie Redemann</author>
    <author>Thomas Müller-Reichert</author>
    <author>Steffen Prohaska</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-30</number>
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  <doc>
    <id>8238</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
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    <language>eng</language>
    <pageFirst>25</pageFirst>
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    <issue>1</issue>
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    <publishedDate>2021-06-10</publishedDate>
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    <title language="eng">Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography</title>
    <abstract language="eng">We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists.</abstract>
    <parentTitle language="eng">Journal of Microscopy</parentTitle>
    <identifier type="doi">10.1111/jmi.13039</identifier>
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    <author>Norbert Lindow</author>
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    <author>Robert Kiewisz</author>
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