@article{EggerDercksenUdvaryetal., author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, series = {Frontiers in Neuroanatomy}, volume = {8}, journal = {Frontiers in Neuroanatomy}, number = {129}, doi = {10.3389/fnana.2014.00129}, language = {en} } @article{LangDercksenSakmannetal.2011, author = {Lang, Stefan and Dercksen, Vincent J. and Sakmann, Bert and Oberlaender, Marcel}, title = {Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex}, series = {Neural Networks}, volume = {24}, journal = {Neural Networks}, number = {9}, doi = {doi:10.1016/j.neunet.2011.06.013}, pages = {998 -- 1011}, year = {2011}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2011, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Light Microscopy-Based Reconstruction and Interactive Structural Analysis of Cortical Neural Networks}, series = {BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization}, journal = {BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization}, year = {2011}, language = {en} } @inproceedings{DercksenEggerHegeetal.2012, author = {Dercksen, Vincent J. and Egger, Robert and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis}, series = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, address = {Norrk{\"o}ping, Sweden}, doi = {10.2312/VCBM/VCBM12/017-024}, pages = {17 -- 24}, year = {2012}, language = {en} } @misc{PfisterKaynigBothaetal.2012, author = {Pfister, Hanspeter and Kaynig, Verena and Botha, Charl and Bruckner, Stefan and Dercksen, Vincent J. and Hege, Hans-Christian and Roerdink, Jos}, title = {Visualization in Connectomics}, doi = {10.1007/978-1-4471-6497-5_21}, year = {2012}, language = {en} } @article{OberlaenderdeKockBrunoetal.2012, author = {Oberlaender, Marcel and de Kock, Christiaan P. J. and Bruno, Randy M. and Ramirez, Alejandro and Meyer, Hanno and Dercksen, Vincent J. and Helmstaedter, Moritz and Sakmann, Bert}, title = {Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex}, series = {Cerebral Cortex}, volume = {22}, journal = {Cerebral Cortex}, number = {10}, doi = {doi:10.1093/cercor/bhr317}, pages = {2375 -- 2391}, year = {2012}, language = {en} } @article{KussGenselMeyeretal.2010, author = {Kuß, Anja and Gensel, Maria and Meyer, Bj{\"o}rn and Dercksen, Vincent J. and Prohaska, Steffen}, title = {Effective Techniques to Visualize Filament-Surface Relationships}, series = {Comput. Graph. Forum}, volume = {29}, journal = {Comput. Graph. Forum}, pages = {1003 -- 1012}, year = {2010}, language = {en} } @article{OberlaenderDercksenEggeretal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Egger, Robert and Gensel, Maria and Sakmann, Bert and Hege, Hans-Christian}, title = {Automated three-dimensional detection and counting of neuron somata}, series = {Journal of Neuroscience Methods}, volume = {180}, journal = {Journal of Neuroscience Methods}, number = {1}, doi = {10.1016/j.jneumeth.2009.03.008}, pages = {147 -- 160}, year = {2009}, language = {en} } @misc{OberlaenderDercksenLangetal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Lang, Stefan and Sakmann, Bert}, title = {3D mapping of synaptic connections within "in silico" microcircuits of full compartmental neurons in extended networks on the example of VPM axons projecting into S1 of rats.}, series = {Conference Abstract, Neuroinformatics}, journal = {Conference Abstract, Neuroinformatics}, doi = {10.3389/conf.neuro.11.2009.08.092}, year = {2009}, language = {en} } @misc{OberlaenderBrunodeKocketal.2009, author = {Oberlaender, Marcel and Bruno, Randy M. and de Kock, Christiaan P. J. and Meyer, Hanno and Dercksen, Vincent J. and Sakmann, Bert}, title = {3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex}, series = {Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)}, journal = {Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)}, year = {2009}, language = {en} } @inproceedings{HombergBinnerProhaskaetal.2009, author = {Homberg, Ulrike and Binner, Richard and Prohaska, Steffen and Dercksen, Vincent J. and Kuß, Anja and Kalbe, Ute}, title = {Determining Geometric Grain Structure from X-Ray Micro-Tomograms of Gradated Soil}, series = {Workshop Internal Erosion}, volume = {21}, booktitle = {Workshop Internal Erosion}, pages = {37 -- 52}, year = {2009}, language = {en} } @article{LandauEggerDercksenetal., author = {Landau, Itamar D. and Egger, Robert and Dercksen, Vincent J. and Oberlaender, Marcel and Sompolinsky, Haim}, title = {The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks}, series = {Neuron}, volume = {92}, journal = {Neuron}, number = {5}, doi = {10.1016/j.neuron.2016.10.027}, pages = {1106 -- 1121}, abstract = {Models of cortical dynamics often assume a homogeneous connectivity structure. However, we show that heterogeneous input connectivity can prevent the dynamic balance between excitation and inhibition, a hallmark of cortical dynamics, and yield unrealistically sparse and temporally regular firing. Anatomically based estimates of the connectivity of layer 4 (L4) rat barrel cortex and numerical simulations of this circuit indicate that the local network possesses substantial heterogeneity in input connectivity, sufficient to disrupt excitation-inhibition balance. We show that homeostatic plasticity in inhibitory synapses can align the functional connectivity to compensate for structural heterogeneity. Alternatively, spike-frequency adaptation can give rise to a novel state in which local firing rates adjust dynamically so that adaptation currents and synaptic inputs are balanced. This theory is supported by simulations of L4 barrel cortex during spontaneous and stimulus-evoked conditions. Our study shows how synaptic and cellular mechanisms yield fluctuation-driven dynamics despite structural heterogeneity in cortical circuits.}, language = {en} } @article{DercksenHegeOberlaender2014, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, series = {NeuroInformatics}, volume = {12}, journal = {NeuroInformatics}, number = {2}, publisher = {Springer US}, doi = {10.1007/s12021-013-9213-2}, pages = {325 -- 339}, year = {2014}, language = {en} } @misc{EggerDercksenKocketal.2014, author = {Egger, Robert and Dercksen, Vincent J. and Kock, Christiaan P.J. and Oberlaender, Marcel}, title = {Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex}, series = {The Computing Dendrite}, volume = {11}, journal = {The Computing Dendrite}, editor = {Cuntz, Hermann and Remme, Michiel W.H. and Torben-Nielsen, Benjamin}, publisher = {Springer}, address = {New York}, doi = {10.1007/978-1-4614-8094-5_8}, pages = {127 -- 145}, year = {2014}, language = {en} } @inproceedings{PapazovDercksenLameckeretal.2008, author = {Papazov, Chavdar and Dercksen, Vincent J. and Lamecker, Hans and Hege, Hans-Christian}, title = {Visualizing morphogenesis and growth by temporal interpolation of surface-based 3D atlases}, series = {Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging}, booktitle = {Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging}, doi = {10.1109/ISBI.2008.4541123}, pages = {824 -- 827}, year = {2008}, language = {en} } @misc{OberlaenderDercksenBroseretal.2008, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Broser, Philip J. and Bruno, Randy M. and Sakmann, Bert}, title = {NeuroMorph and NeuroCount: Automated tools for fast and objective acquisition of neuronal morphology for quantitative structural analysis}, series = {Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics}, journal = {Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics}, doi = {10.3389/conf.neuro.11.2008.01.065}, year = {2008}, language = {en} } @inproceedings{DercksenWeberGuentheretal.2009, author = {Dercksen, Vincent J. and Weber, Britta and G{\"u}nther, David and Oberlaender, Marcel and Prohaska, Steffen and Hege, Hans-Christian}, title = {Automatic alignment of stacks of filament data}, series = {Proc. IEEE International Symposium on Biomedical Imaging}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging}, publisher = {IEEE press}, address = {Boston, USA}, pages = {971 -- 974}, year = {2009}, language = {en} } @misc{DercksenGenselKuss2009, author = {Dercksen, Vincent J. and Gensel, Maria and Kuß, Anja}, title = {Visual Accentuation of Spatial Relationships between Filamentous and Voluminous Surface Structures}, series = {Conference Abstract, Eurographics / IEEE Symposium on Visualization}, journal = {Conference Abstract, Eurographics / IEEE Symposium on Visualization}, year = {2009}, language = {en} } @incollection{DercksenBruessStallingetal.2008, author = {Dercksen, Vincent J. and Br{\"u}ß, Cornelia and Stalling, Detlev and Gubatz, Sabine and Seiffert, Udo and Hege, Hans-Christian}, title = {Towards automatic generation of 3D models of biological objects based on serial sections}, series = {Visualization in Medicine and Life Sciences}, booktitle = {Visualization in Medicine and Life Sciences}, publisher = {Springer-Verlag Berlin Heidelberg}, doi = {10.1007/978-3-540-72630-2}, pages = {3 -- 25}, year = {2008}, language = {en} } @inproceedings{DercksenProhaskaHege2005, author = {Dercksen, Vincent J. and Prohaska, Steffen and Hege, Hans-Christian}, title = {Fast cross-sectional display of large data sets}, series = {IAPR Conference on Machine Vision Applications}, booktitle = {IAPR Conference on Machine Vision Applications}, address = {Tsukuba, Japan}, pages = {336 -- 339}, year = {2005}, language = {en} } @article{GubatzDercksenBruessetal.2007, author = {Gubatz, Sabine and Dercksen, Vincent J. and Br{\"u}ß, Cornelia and Weschke, Winfriede and Wobus, Ulrich}, title = {Analysis of barley (hordeum vulgare) grain development using three-dimensional digital models}, series = {The Plant Journal}, volume = {52}, journal = {The Plant Journal}, number = {4}, doi = {10.1111/j.1365-313X.2007.03260.x}, pages = {779 -- 790}, year = {2007}, language = {en} } @incollection{PfisterKaynigBothaetal.2014, author = {Pfister, Hanspeter and Kaynig, Verena and Botha, Charl P. and Bruckner, Stefan and Dercksen, Vincent J. and Hege, Hans-Christian and Roerdink, Jos B.T.M.}, title = {Visualization in Connectomics}, series = {Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization}, booktitle = {Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization}, editor = {Hansen, Charles D. and Chen, Min and Johnson, Christopher R. and Kaufman, Arie E. and Hagen, Hans}, publisher = {Springer}, isbn = {978-1-4471-6496-8}, doi = {10.1007/978-1-4471-6497-5_21}, pages = {221 -- 245}, year = {2014}, abstract = {Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures. This representation can be used to understand how functional brain states emerge from their underlying anatomical structures and how dysfunction and neuronal diseases arise. We review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe a number of challenges. After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity information from image data at macro-, meso- and microscales. We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks.}, language = {en} } @article{LindowBruenigDercksenetal., 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}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2020.05.28.120899}, abstract = {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.}, language = {en} } @misc{LindowBruenigDercksenetal., 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}, title = {Semi-automatic Stitching of Serial Section Image Stacks with Filamentous Structures}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-73739}, abstract = {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. 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. 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.}, language = {en} } @misc{EggerDercksenUdvaryetal., author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, issn = {1438-0064}, doi = {10.3389/fnana.2014.00129}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-53075}, abstract = {Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and sub-cellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.}, language = {en} } @misc{DercksenHegeOberlaender2013, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, issn = {1438-0064}, doi = {10.1007/s12021-013-9213-2}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43157}, year = {2013}, abstract = {Neuroanatomical analysis, such as classification of cell types, depends on reliable reconstruction of large numbers of complete 3D dendrite and axon morphologies. At present, the majority of neuron reconstructions are obtained from preparations in a single tissue slice in vitro, thus suffering from cut off dendrites and, more dramatically, cut off axons. In general, axons can innervate volumes of several cubic millimeters and may reach path lengths of tens of centimeters. Thus, their complete reconstruction requires in vivo labeling, histological sectioning and imaging of large fields of view. Unfortunately, anisotropic background conditions across such large tissue volumes, as well as faintly labeled thin neurites, result in incomplete or erroneous automated tracings and even lead experts to make annotation errors during manual reconstructions. Consequently, tracing reliability renders the major bottleneck for reconstructing complete 3D neuron morphologies. Here, we present a novel set of tools, integrated into a software environment named 'Filament Editor', for creating reliable neuron tracings from sparsely labeled in vivo datasets. The Filament Editor allows for simultaneous visualization of complex neuronal tracings and image data in a 3D viewer, proof-editing of neuronal tracings, alignment and interconnection across sections, and morphometric analysis in relation to 3D anatomical reference structures. We illustrate the functionality of the Filament Editor on the example of in vivo labeled axons and demonstrate that for the exemplary dataset the final tracing results after proof-editing are independent of the expertise of the human operator.}, language = {en} } @misc{OezelKulkarniHasanetal., author = {{\"O}zel, M. Neset and Kulkarni, Abhishek and Hasan, Amr and Brummer, Josephine and Moldenhauer, Marian and Daumann, Ilsa-Maria and Wolfenberg, Heike and Dercksen, Vincent J. and Kiral, F. Ridvan and Weiser, Martin and Prohaska, Steffen and von Kleist, Max and Hiesinger, Peter Robin}, title = {Serial synapse formation through filopodial competition for synaptic seeding factors}, issn = {1438-0064}, doi = {10.1016/j.devcel.2019.06.014}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-74397}, abstract = {Following axon pathfinding, growth cones transition from stochastic filopodial exploration to the formation of a limited number of synapses. How the interplay of filopodia and synapse assembly ensures robust connectivity in the brain has remained a challenging problem. Here, we developed a new 4D analysis method for filopodial dynamics and a data-driven computational model of synapse formation for R7 photoreceptor axons in developing Drosophila brains. Our live data support a 'serial synapse formation' model, where at any time point only a single 'synaptogenic' filopodium suppresses the synaptic competence of other filopodia through competition for synaptic seeding factors. Loss of the synaptic seeding factors Syd-1 and Liprin-α leads to a loss of this suppression, filopodial destabilization and reduced synapse formation, which is sufficient to cause the destabilization of entire axon terminals. Our model provides a filopodial 'winner-takes-all' mechanism that ensures the formation of an appropriate number of synapses.}, language = {en} } @article{OzelKulkarniHasanetal., author = {Ozel, Mehmet Neset and Kulkarni, Abhishek and Hasan, Amr and Brummer, Josephine and Moldenhauer, Marian and Daumann, Ilsa-Maria and Wolfenberg, Heike and Dercksen, Vincent J. and Kiral, Ferdi Ridvan and Weiser, Martin and Prohaska, Steffen and von Kleist, Max and Hiesinger, Peter Robin}, title = {Serial synapse formation through filopodial competition for synaptic seeding factors}, series = {Developmental Cell}, volume = {50}, journal = {Developmental Cell}, number = {4}, doi = {10.1016/j.devcel.2019.06.014}, pages = {447 -- 461}, abstract = {Following axon pathfinding, growth cones transition from stochastic filopodial exploration to the formation of a limited number of synapses. How the interplay of filopodia and synapse assembly ensures robust connectivity in the brain has remained a challenging problem. Here, we developed a new 4D analysis method for filopodial dynamics and a data-driven computational model of synapse formation for R7 photoreceptor axons in developing Drosophila brains. Our live data support a 'serial synapse formation' model, where at any time point only a single 'synaptogenic' filopodium suppresses the synaptic competence of other filopodia through competition for synaptic seeding factors. Loss of the synaptic seeding factors Syd-1 and Liprin-α leads to a loss of this suppression, filopodial destabilization and reduced synapse formation, which is sufficient to cause the destabilization of entire axon terminals. Our model provides a filopodial 'winner-takes-all' mechanism that ensures the formation of an appropriate number of synapses.}, language = {en} } @article{LindowBruenigDercksenetal., 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}, series = {Journal of Microscopy}, volume = {284}, journal = {Journal of Microscopy}, number = {1}, doi = {10.1111/jmi.13039}, pages = {25 -- 44}, 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} } @phdthesis{Dercksen2015, author = {Dercksen, Vincent J.}, title = {Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks}, year = {2015}, abstract = {To understand how the brain translates sensory input into behavior, one needs to identify, at the cellular level, the involved neural circuitry and the electrical signals it carries. This thesis describes methods and tools that enable neuroscientists to obtain important anatomical data, including neuron numbers and shapes, from 3D microscopy images. On this basis, tools have been developed to create and visually analyze anatomically realistic 3D models of neural networks: 1. An automatic segmentation method for determining the number and location of neuron cell bodies in 3D microscopy images. Application of this method yields a difference of merely ∼4\% between automatically and manually counted cells, which is sufficiently accurate for application in large-scale counting experiments. 2. A method for the automatic alignment of 3D section volumes containing filamentous structures. To this end, an existing point-matching-based method has been adapted such that sections containing neuron and microtubule fragments could be successfully aligned. 3. The Filament Editor, a 3D proof-editing tool for visual verification and correction of automatically traced filaments. The usefulness of the Filament Editor is demonstrated by applying it in a validated neuron reconstruction pipeline to create 3D models of long-range and complex neuronal branches. 4. The tool NeuroNet, which is used to assemble an anatomical model of a neural network representing the rat barrel cortex (or subnetworks therein, e.g. individual cortical columns), based on reconstructed anatomical data, such as neuron distributions and 3D morphologies. The tool estimates synaptic connectivity between neurons based on structural overlap between axons and dendrites. 5. A framework for the interactive visual analysis of synaptic connectivity in such networks at multiple scales. It works from the level of neuron populations down to individual synapse positions on dendritic trees. It comprises the Cortical Column Connectivity Viewer, developed to analyze synaptic connections between neuron populations within and between cortical columns. The usefulness of these methods is demonstrated by applying them to reconstruct and analyze neural networks in the rat barrel cortex. Finally, I describe several applications of these methods and tools by neuroscientists, yielding significant biological findings regarding neuron anatomy and connectivity.}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2012, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Interactive Visualization - a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks}, series = {Visualization in Medicine and Life Sciences II}, journal = {Visualization in Medicine and Life Sciences II}, editor = {Linsen, Lars and Hagen, Hans and Hamann, Bernd and Hege, Hans-Christian}, publisher = {Springer, Berlin}, pages = {27 -- 44}, year = {2012}, language = {en} }