@article{ProhaskaDreherDewhirstetal.2004, author = {Prohaska, Steffen and Dreher, Matthew and Dewhirst, Mark and Chilkoti, Ashutosh and Pries, Axel}, title = {3-D reconstruction of tumor vascular networks}, volume = {41}, journal = {J. Vas. Res.}, pages = {463}, year = {2004}, language = {en} } @article{HutanuAllenBecketal.2006, author = {Hutanu, Andrei and Allen, Gabrielle and Beck, Stephen and Holub, Petr and Kaiser, Hartmut and Kulshrestha, Archit and Liska, Milos and MacLaren, Jon and Matyska, Ludek and Paruchuri, Ravi and Prohaska, Steffen and Seidel, Edward and Ullmer, Brygg and Venkataraman, Shalini}, title = {Distributed and collaborative visualization of large data sets using high-speed networks}, volume = {22(8)}, journal = {Future Generation Comp. Syst}, doi = {10.1016/j.future.2006.03.026}, pages = {1004 -- 1010}, year = {2006}, language = {en} } @article{HegeWeinkaufProhaskaetal.2005, author = {Hege, Hans-Christian and Weinkauf, Tino and Prohaska, Steffen and Hutanu, Andrei}, title = {Towards distributed visualization and analysis of large flow data}, volume = {48 (2)}, journal = {JSME International Journal, Series B}, pages = {241 -- 246}, year = {2005}, language = {en} } @article{ThomsenLaibKolleretal.2005, author = {Thomsen, Jesper and Laib, Andreas and Koller, Bruno and Prohaska, Steffen and Mosekilde, L. and Gowin, Wolfgang}, title = {Stereological measures of trabecular bone structure: Comparison of 3D micro computed tomography with 2D histological sections in human proximal tibial bone biopsies}, volume = {218}, journal = {Journal of Microscopy}, pages = {171 -- 179}, year = {2005}, language = {en} } @article{FouardMalandainProhaskaetal.2006, author = {Fouard, C{\´e}line and Malandain, Gr{\´e}goire and Prohaska, Steffen and Westerhoff, Malte}, title = {Blockwise processing applied to brain micro-vascular network study}, volume = {25}, journal = {IEEE Transactions on Medical Imaging}, number = {10}, doi = {10.1109/TMI.2006.880670}, pages = {1319 -- 1328}, year = {2006}, language = {en} } @article{ZaikinSaparinKurthsetal.2005, author = {Zaikin, Alexei and Saparin, Peter and Kurths, J{\"u}rgen and Prohaska, Steffen and Gowin, Wolfgang}, title = {Modeling resorption in 2D-CT and 3D μ-CT bone images}, volume = {15(9)}, journal = {Int. J. of Bif. and Chaos}, pages = {2995 -- 3009}, year = {2005}, language = {en} } @article{WeberTranfieldHoeoegetal.2014, author = {Weber, Britta and Tranfield, Erin M. and H{\"o}{\"o}g, Johanna L. and Baum, Daniel and Antony, Claude and Hyman, Tony and Verbavatz, Jean-Marc and Prohaska, Steffen}, title = {Automated stitching of microtubule centerlines across serial electron tomograms}, journal = {PLoS ONE}, doi = {10.1371/journal.pone.0113222}, pages = {e113222}, year = {2014}, language = {en} } @article{HoerthBaumKnoeteletal.2015, author = {Hoerth, Rebecca M. and Baum, Daniel and Kn{\"o}tel, David and Prohaska, Steffen and Willie, Bettina M. and Duda, Georg and Hege, Hans-Christian and Fratzl, Peter and Wagermaier, Wolfgang}, title = {Registering 2D and 3D Imaging Data of Bone during Healing}, volume = {56}, journal = {Connective Tissue Research}, number = {2}, publisher = {Taylor \& Francis}, doi = {10.3109/03008207.2015.1005210}, pages = {133 -- 143}, year = {2015}, language = {en} } @article{HombergBaumProhaskaetal.2017, author = {Homberg, Ulrike and Baum, Daniel and Prohaska, Steffen and G{\"u}nster, Jens and Krauß-Sch{\"u}ler, Stefanie}, title = {Adapting trabecular structures for 3D printing: an image processing approach based on µCT data}, volume = {3}, journal = {Biomedical Physics \& Engineering Express}, number = {3}, publisher = {IOP Publishing}, doi = {10.1088/2057-1976/aa7611}, year = {2017}, abstract = {Materials with a trabecular structure notably combine advantages such as lightweight, reasonable strength, and permeability for fluids. This combination of advantages is especially interesting for tissue engineering in trauma surgery and orthopedics. Bone-substituting scaffolds for instance are designed with a trabecular structure in order to allow cell migration for bone ingrowth and vascularization. An emerging and recently very popular technology to produce such complex, porous structures is 3D printing. However, several technological aspects regarding the scaffold architecture, the printable resolution, and the feature size have to be considered when fabricating scaffolds for bone tissue replacement and regeneration. Here, we present a strategy to assess and prepare realistic trabecular structures for 3D printing using image analysis with the aim of preserving the structural elements. We discuss critical conditions of the printing system and present a 3-stage approach to adapt a trabecular structure from \$\mu\$CT data while incorporating knowledge about the printing system. In the first stage, an image-based extraction of solid and void structures is performed, which results in voxel- and graph-based representations of the extracted structures. These representations not only allow us to quantify geometrical properties such as pore size or strut geometry and length. But, since the graph represents the geometry and the topology of the initial structure, it can be used in the second stage to modify and adjust feature size, volume and sample size in an easy and consistent way. In the final reconstruction stage, the graph is then converted into a voxel representation preserving the topology of the initial structure. This stage generates a model with respect to the printing conditions to ensure a stable and controlled voxel placement during the printing process.}, language = {en} } @article{RedemannBaumgartLindowetal.2017, author = {Redemann, Stefanie and Baumgart, Johannes and Lindow, Norbert and Shelley, Michael and Nazockdast, Ehssan and Kratz, Andrea and Prohaska, Steffen and Brugu{\´e}s, Jan and F{\"u}rthauer, Sebastian and M{\"u}ller-Reichert, Thomas}, title = {C. elegans chromosomes connect to centrosomes by anchoring into the spindle network}, volume = {8}, journal = {Nature Communications}, number = {15288}, doi = {10.1038/ncomms15288}, year = {2017}, abstract = {The mitotic spindle ensures the faithful segregation of chromosomes. Here we combine the first large-scale serial electron tomography of whole mitotic spindles in early C. elegans embryos with live-cell imaging to reconstruct all microtubules in 3D and identify their plus- and minus-ends. We classify them as kinetochore (KMTs), spindle (SMTs) or astral microtubules (AMTs) according to their positions, and quantify distinct properties of each class. While our light microscopy and mutant studies show that microtubules are nucleated from the centrosomes, we find only a few KMTs directly connected to the centrosomes. Indeed, by quantitatively analysing several models of microtubule growth, we conclude that minus-ends of KMTs have selectively detached and depolymerized from the centrosome. In toto, our results show that the connection between centrosomes and chromosomes is mediated by an anchoring into the entire spindle network and that any direct connections through KMTs are few and likely very transient.}, language = {en} } @article{PaetschBaumProhaskaetal.2014, author = {Paetsch, Olaf and Baum, Daniel and Prohaska, Steffen and Ehrig, Karsten and Ebell, Gino and Meinel, Dietmar and Heyn, Andreas}, title = {Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben}, journal = {DGZfP-Jahrestagung 2014 Konferenzband}, year = {2014}, language = {de} } @article{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0188018}, year = {2017}, abstract = {Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.}, language = {en} } @article{LindowBruenigDercksenetal.2021, author = {Lindow, Norbert and Br{\"u}nig, Florian and Dercksen, Vincent J. and Fabig, Gunar and Kiewisz, Robert and Redemann, Stefanie and M{\"u}ller-Reichert, Thomas and Prohaska, Steffen and Baum, Daniel}, title = {Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography}, volume = {284}, journal = {Journal of Microscopy}, number = {1}, doi = {10.1111/jmi.13039}, pages = {25 -- 44}, year = {2021}, abstract = {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.}, language = {en} } @article{LantzschYuChenetal.2021, author = {Lantzsch, Ina and Yu, Che-Hang and Chen, Yu-Zen and Zimyanin, Vitaly and Yazdkhasti, Hossein and Lindow, Norbert and Szentgyoergyi, Erik and Pani, Ariel M and Prohaska, Steffen and Srayko, Martin and F{\"u}rthauer, Sebastian and Redemann, Stefanie}, title = {Microtubule reorganization during female meiosis in C. elegans}, volume = {10}, journal = {eLife}, doi = {10.7554/eLife.58903}, pages = {e58903}, year = {2021}, abstract = {Most female meiotic spindles undergo striking morphological changes while transitioning from metaphase to anaphase. The ultra-structure of meiotic spindles, and how changes to this structure correlate with such dramatic spindle rearrangements remains largely unknown. To address this, we applied light microscopy, large-scale electron tomography and mathematical modeling of female meiotic \textit{Caenorhabditis elegans} spindles. Combining these approaches, we find that meiotic spindles are dynamic arrays of short microtubules that turn over within seconds. The results show that the metaphase to anaphase transition correlates with an increase in microtubule numbers and a decrease in their average length. Detailed analysis of the tomographic data revealed that the microtubule length changes significantly during the metaphase-to-anaphase transition. This effect is most pronounced for microtubules located within 150 nm of the chromosome surface. To understand the mechanisms that drive this transition, we developed a mathematical model for the microtubule length distribution that considers microtubule growth, catastrophe, and severing. Using Bayesian inference to compare model predictions and data, we find that microtubule turn-over is the major driver of the spindle reorganizations. Our data suggest that in metaphase only a minor fraction of microtubules, those closest to the chromosomes, are severed. The large majority of microtubules, which are not in close contact with chromosomes, do not undergo severing. Instead, their length distribution is fully explained by growth and catastrophe. This suggests that the most prominent drivers of spindle rearrangements are changes in nucleation and catastrophe rate. In addition, we provide evidence that microtubule severing is dependent on katanin.}, language = {en} }