TY - CHAP A1 - Dercksen, Vincent J. A1 - Weber, Britta A1 - Günther, David A1 - Oberlaender, Marcel A1 - Prohaska, Steffen A1 - Hege, Hans-Christian T1 - Automatic alignment of stacks of filament data T2 - Proc. IEEE International Symposium on Biomedical Imaging Y1 - 2009 SP - 971 EP - 974 PB - IEEE press CY - Boston, USA ER - TY - JOUR A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Egger, Robert A1 - Gensel, Maria A1 - Sakmann, Bert A1 - Hege, Hans-Christian T1 - Automated three-dimensional detection and counting of neuron somata JF - Journal of Neuroscience Methods Y1 - 2009 U6 - https://doi.org/10.1016/j.jneumeth.2009.03.008 VL - 180 IS - 1 SP - 147 EP - 160 ER - TY - CHAP A1 - Papazov, Chavdar A1 - Dercksen, Vincent J. A1 - Lamecker, Hans A1 - Hege, Hans-Christian T1 - Visualizing morphogenesis and growth by temporal interpolation of surface-based 3D atlases T2 - Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging Y1 - 2008 U6 - https://doi.org/10.1109/ISBI.2008.4541123 SP - 824 EP - 827 ER - TY - GEN A1 - Dercksen, Vincent J. A1 - Gensel, Maria A1 - Kuß, Anja T1 - Visual Accentuation of Spatial Relationships between Filamentous and Voluminous Surface Structures T2 - Conference Abstract, Eurographics / IEEE Symposium on Visualization Y1 - 2009 ER - TY - JOUR A1 - Gubatz, Sabine A1 - Dercksen, Vincent J. A1 - Brüß, Cornelia A1 - Weschke, Winfriede A1 - Wobus, Ulrich T1 - Analysis of barley (hordeum vulgare) grain development using three-dimensional digital models JF - The Plant Journal Y1 - 2007 U6 - https://doi.org/10.1111/j.1365-313X.2007.03260.x VL - 52 IS - 4 SP - 779 EP - 790 ER - TY - CHAP A1 - Dercksen, Vincent J. A1 - Prohaska, Steffen A1 - Hege, Hans-Christian T1 - Fast cross-sectional display of large data sets T2 - IAPR Conference on Machine Vision Applications Y1 - 2005 SP - 336 EP - 339 CY - Tsukuba, Japan ER - TY - JOUR A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data JF - Frontiers in Neuroanatomy Y1 - 2014 U6 - https://doi.org/10.3389/fnana.2014.00129 VL - 8 IS - 129 ER - TY - CHAP A1 - Pfister, Hanspeter A1 - Kaynig, Verena A1 - Botha, Charl P. A1 - Bruckner, Stefan A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Roerdink, Jos B.T.M. ED - Hansen, Charles D. ED - Chen, Min ED - Johnson, Christopher R. ED - Kaufman, Arie E. ED - Hagen, Hans T1 - Visualization in Connectomics T2 - Scientific Visualization - Uncertainty, Multifield, Biomedical, and Scalable Visualization N2 - 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. Y1 - 2014 SN - 978-1-4471-6496-8 U6 - https://doi.org/10.1007/978-1-4471-6497-5_21 SP - 221 EP - 245 PB - Springer ER - TY - GEN A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data N2 - 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. T3 - ZIB-Report - 14-43 KW - 3D neural network KW - Dense connectome KW - Reconstruction Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53075 SN - 1438-0064 ER - TY - THES A1 - Dercksen, Vincent J. T1 - Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks N2 - 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. KW - 3D reconstruction KW - image segmentation KW - neural network KW - neuron tracing KW - connectome Y1 - 2015 UR - http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000100938 ER -