Prof. Dr. Timo Dickscheid

Working Group Leader "Big Data Analytics"

Forschungsthemen

Computer Vision, Biomedical Imaging, Neuroinformatics, Artificial Intelligence

Kontakt

+49 2461/61-1763

+49 2461/61-3483

E-Mail

ORCID

Helmholtz AI research group

Heinrich Heine University Düsseldorf

Adresse

Forschungszentrum Jülich GmbH
Wilhelm-Johnen-Straße
52428 Jülich

Institut für Neurowissenschaften und Medizin (INM)

Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Gebäude 15.9 / Raum 4009

Sie können uns hier finden

Research focus

Contributing to the development of a highly detailed and openly accessible human brain atlas at cellular resolution, my research interests are at the intersection of Computer Vision, Big Data Analytics, and Neuroinformatics.

My research group in Jülich investigates methods for extracting information from microscopic images, and reconstructing 3D brain models at the level of individual cells and nerve fibers. This includes Machine Learning methods for microscopic image analysis that can learn from limited training data, and be applied to data streams from high-throughput imaging in the order of Terabytes per day. To deal with such data volumes, we also address the data lifecycle “from the lab to the web” - distributed data management, high performance computing workflows, and web services to provide remote access to large 3D image data.

I teach topics from computer vision and machine learning at Heinrich Heine University Düsseldorf , and lead the Helmholtz AI unit for the research field "Information" together with Stefan Kesselheim from the Jülich Supercomputing center.

My team and I contribute to the development of the European research infrastructure EBRAINS in the frame of the Human Brain Project by building the technology for a highly detailed human brain atlas together with partners in different European research institutions, and we are part of the Helmholtz International Lab HIBALL with partners at McGill and Mila in Montréal, Canada.

Publikationen

Upschulte, Eric, Stefan Harmeling, Katrin Amunts, und Timo Dickscheid. 2022. „Contour Proposal Networks for Biomedical Instance Segmentation“. Medical Image Analysis, Januar, 102371. https://doi.org/10.1016/j.media.2022.102371.

Vaca, Esteban, Miriam Menzel, Katrin Amunts, Markus Axer, und Timo Dickscheid. 2022. „GORDA: Graph-Based Orientation Distribution Analysis of SLI Scatterometry Patterns of Nerve Fibres“. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI52829.2022.9761492.

Albada, S. J. van, A. Morales-Gregorio, T. Dickscheid, A. Goulas, R. Bakker, S. Bludau, G. Palm, C.-C. Hilgetag, und M. Diesmann. 2022. „Bringing Anatomical Information into Neuronal Network Models“. In Computational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks, 201–34. Advances in Experimental Medicine and Biology. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-89439-9_9.

Schiffer, Christian, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts, und Timo Dickscheid. 2021. „Convolutional Neural Networks for Cytoarchitectonic Brain Mapping at Large Scale“. NeuroImage 240 (Oktober): 118327. https://doi.org/10.1016/j.neuroimage.2021.118327.

Schiffer, Christian, Stefan Harmeling, Katrin Amunts, und Timo Dickscheid. „2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks“. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, https://doi.org/10.1007/978-3-030-87237-3_38

Schiffer, Christian, Katrin Amunts, Stefan Harmeling, und Timo Dickscheid. 2021. „Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections“. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 603–6. https://doi.org/10.1109/ISBI48211.2021.9433986.

Schiffer, Christian, Luisa Schuhmacher, Katrin Amunts, und T. Dickscheid. 2021. „Learning to predict cutting angles from histological human brain sections“. In International conference on Medical Imaging with Deep Learning (MIDL 2021)

Kiwitz, Kai, Christian Schiffer, Hannah Spitzer, Timo Dickscheid, und Katrin Amunts. 2020. „Deep Learning Networks Reflect Cytoarchitectonic Features Used in Brain Mapping“. Scientific Reports 10 (1): 1–15. https://doi.org/10.1038/s41598-020-78638-y.

Wagstyl, Konrad, Stéphanie Larocque, Guillem Cucurull, Claude Lepage, Joseph Paul Cohen, Sebastian Bludau, Nicola Palomero-Gallagher, u. a. 2020. „BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices“. PLoS biology 18 (4): e3000678. https://doi.org/10.1371/journal.pbio.3000678.

Dickscheid, Timo, Sarah Haas, Sebastian Bludau, Philipp Glock, Marcel Huysegoms, und Katrin Amunts. 2019. „Towards 3D Reconstruction of Neuronal Cell Distributions from Histological Human Brain Sections“. Advances in Parallel Computing 34 (Future Trends of HPC in a Disruptive Scenario): 223–39. https://doi.org/10.3233/APC190016.

Oden, L., C. Schiffer, H. Spitzer, T. Dickscheid, und D. Pleiter. 2019. „IO Challenges for Human Brain Atlasing Using Deep Learning Methods - An In-Depth Analysis“. In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 291–98. https://doi.org/10.1109/EMPDP.2019.8671630.

Spitzer, Hannah, Katrin Amunts, Stefan Harmeling, und Timo Dickscheid. 2018. „Compact feature representations for human brain cytoarchitecture using self-supervised learning“. In International conference on Medical Imaging with Deep Learning (MIDL 2018)

Spitzer, Hannah, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, und Timo Dickscheid. 2018. „Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-Supervised Siamese Networks“. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 663–71. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-00931-1_76.

Lebenberg, J., M. Labit, G. Auzias, H. Mohlberg, C. Fischer, D. Rivière, E. Duchesnay, u. a. 2018. „A Framework Based on Sulcal Constraints to Align Preterm, Infant and Adult Human Brain Images Acquired in Vivo and Post Mortem“. Brain Structure and Function 223 (9): 4153–68. https://doi.org/10.1007/s00429-018-1735-9.

Dickscheid, Timo, und Wolfgang Förstner. 2013. „A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships“. Photogrammetrie - Fernerkundung - Geoinformation 2013 (4): 269–83. https://doi.org/10.1127/1432-8364/2013/0176.

Spitzer, H., K. Amunts, S. Harmeling, und T. Dickscheid. 2017. „Parcellation of visual cortex on high-resolution histological brain sections using convolutional neural networks“. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 920–23. https://doi.org/10.1109/ISBI.2017.7950666.

Amunts, Katrin, Claude Lepage, Louis Borgeat, Hartmut Mohlberg, Timo Dickscheid, Marc-Étienne Rousseau, Sebastian Bludau, u. a. 2013. „BigBrain: An Ultrahigh-Resolution 3D Human Brain Model“. Science 340 (6139): 1472–75. https://doi.org/10.1126/science.1235381.

Kleiner, Melanie, Markus Axer, David Gräßel, Julia Reckfort, Uwe Pietrzyk, Katrin Amunts, und Timo Dickscheid. 2012. „Classification of Ambiguous Nerve Fiber Orientations in 3D Polarized Light Imaging“. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. https://doi.org/10.1007/978-3-642-33415-3_26.

Dickscheid, Timo, Falko Schindler, und Wolfgang Förstner. 2011. „Coding Images with Local Features“. International Journal of Computer Vision 94 (2): 154–74. https://doi.org/10.1007/s11263-010-0340-z.

Förstner, Wolfgang, Timo Dickscheid, und Falko Schindler. 2009. „On the completeness of coding with image features“. In Procedings of the British Machine Vision Conference 2009. British Machine Vision Association. https://doi.org/10.5244/c.23.1.

Förstner, W., T. Dickscheid, und F. Schindler. 2009. „Detecting interpretable and accurate scale-invariant keypoints“. In 2009 IEEE 12th International Conference on Computer Vision, 2256–63. https://doi.org/10.1109/ICCV.2009.5459458.

Dickscheid, Timo, Thomas Läbe, und Wolfgang Förstner. 2008. „Benchmarking Automatic Bundle Adjustment Results“. In 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), 7–12, Part B3a. Beijing, China. http://www.ipb.uni-bonn.de/pdfs/Dickscheid2008Benchmarking.pdf.

Letzte Änderung: 04.09.2023