@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}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging}, publisher = {IEEE press}, address = {Boston, USA}, pages = {971 -- 974}, year = {2009}, 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}, volume = {180}, journal = {Journal of Neuroscience Methods}, number = {1}, doi = {10.1016/j.jneumeth.2009.03.008}, pages = {147 -- 160}, year = {2009}, 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}, 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{DercksenGenselKuss2009, author = {Dercksen, Vincent J. and Gensel, Maria and Kuß, Anja}, title = {Visual Accentuation of Spatial Relationships between Filamentous and Voluminous Surface Structures}, journal = {Conference Abstract, Eurographics / IEEE Symposium on Visualization}, year = {2009}, 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}, volume = {52}, journal = {The Plant Journal}, number = {4}, doi = {10.1111/j.1365-313X.2007.03260.x}, pages = {779 -- 790}, year = {2007}, language = {en} } @inproceedings{DercksenProhaskaHege2005, author = {Dercksen, Vincent J. and Prohaska, Steffen and Hege, Hans-Christian}, title = {Fast cross-sectional display of large data sets}, booktitle = {IAPR Conference on Machine Vision Applications}, address = {Tsukuba, Japan}, pages = {336 -- 339}, year = {2005}, language = {en} } @article{EggerDercksenUdvaryetal.2014, 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}, volume = {8}, journal = {Frontiers in Neuroanatomy}, number = {129}, doi = {10.3389/fnana.2014.00129}, year = {2014}, 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}, 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}, arxiv = {http://arxiv.org/abs/1206.1428}, 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} } @misc{EggerDercksenUdvaryetal.2014, 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}, year = {2014}, 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} } @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} } @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{BrenceBrummerDercksenetal.2025, author = {Brence, Blaž and Brummer, Josephine and Dercksen, Vincent J. and {\"O}zel, Mehmet Neset and Kulkarni, Abhishkek and Wolterhoff, Neele and Prohaska, Steffen and Hiesinger, Peter Robin and Baum, Daniel}, title = {Semi-automatic Geometrical Reconstruction and Analysis of Filopodia Dynamics in 4D Two-Photon Microscopy Images}, journal = {bioRxiv}, doi = {10.1101/2025.05.20.654789}, year = {2025}, abstract = {Background: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. Results: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. Conclusion: To facilitate simple application within Amira or other frameworks, we share the source code, which is available athttps://github.com/zibamira/filopodia-tool.}, language = {en} }