Publikationen

Publikationen

Computer Vision

Diese Seite führt ausgewählte Publikationen von Prof. Timo Dickscheid auf. Für eine vollständige Liste siehe google scholar und orcid.

  • Oberstrass, A.; Vaca, E.; Upschulte, E.; Niu, M.; Palomero-Gallagher, N.; Graessel, D.; Schiffer, C.; Axer, M.; Amunts, K.; Dickscheid, T. From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging. Imaging Neuroscience 2025. https://doi.org/10.1162/IMAG.a.1079.
  • Dickscheid, T.; Gui, X.; Simsek, A. N.; Schiffer, C.; Mangin, J.-F.; Leprince, Y.; Jirsa, V.; Bjaalie, J. G.; Leergaard, T. B.; Bludau, S.; Amunts, K. Siibra: A Software Tool Suite for Realizing a Multilevel Human Brain Atlas from Complex Data Resources. bioRxiv May 25, 2025, p 2025.05.20.655042. https://doi.org/10.1101/2025.05.20.655042.
  • Wang, X.; Leprince, Y.; Lebenberg, J.; Langlet, C.; Mohlberg, H.; Rivière, D.; Auzias, G.; Dickscheid, T.; Amunts, K.; Mangin, J.-F. A Framework to Improve the Alignment of Individual Cytoarchitectonic Maps of the Julich-Brain Atlas Using Cortical Folding Landmarks. Cerebral Cortex 2024, 34 (2), bhad538. https://doi.org/10.1093/cercor/bhad538.
  • Oberstrass, A.; Muenzing, S. E. A.; Niu, M.; Palomero-Gallagher, N.; Schiffer, C.; Axer, M.; Amunts, K.; Dickscheid, T. Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI. Imaging Neuroscience 2024. https://doi.org/10.1162/imag_a_00351.
  • Oberstrass, A.; DeKraker, J.; Palomero-Gallagher, N.; Muenzing, S. E. A.; Evans, A. C.; Axer, M.; Amunts, K.; Dickscheid, T. Analyzing Regional Organization of The Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI); IEEE: Athens, Greece, 2024; pp 1–5. https://doi.org/10.1109/ISBI56570.2024.10635467.
  • Nebli, A.; Schiffer, C.; Niu, M.; Palomero-Gallagher, N.; Amunts, K.; Dickscheid, T. Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain. Neuroinform 2024. https://doi.org/10.1007/s12021-024-09673-7.
  • Ma, J.; Xie, R.; Ayyadhury, S.; Ge, C.; Gupta, A.; Gupta, R.; Gu, S.; Zhang, Y.; Lee, G.; Kim, J.; Lou, W.; Li, H.; Upschulte, E.; Dickscheid, T.; De Almeida, J. G.; Wang, Y.; Han, L.; Yang, X.; Labagnara, M.; Gligorovski, V.; Scheder, M.; Rahi, S. J.; Kempster, C.; Pollitt, A.; Espinosa, L.; Mignot, T.; Middeke, J. M.; Eckardt, J.-N.; Li, W.; Li, Z.; Cai, X.; Bai, B.; Greenwald, N. F.; Van Valen, D.; Weisbart, E.; Cimini, B. A.; Cheung, T.; Brück, O.; Bader, G. D.; Wang, B. The Multimodality Cell Segmentation Challenge: Toward Universal Solutions. Nat Methods 2024. https://doi.org/10.1038/s41592-024-02233-6.
  • Kropp, J.-O.; Schiffer, C.; Amunts, K.; Dickscheid, T. Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in The Human Brain. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI); IEEE: Athens, Greece, 2024; pp 1–5. https://doi.org/10.1109/ISBI56570.2024.10635384.
  • Kedo, O.; Bludau, S.; Schiffer, C.; Mohlberg, H.; Dickscheid, T.; Amunts, K. Cytoarchitectonic Analysis and 3D Maps of the Mesial Piriform Region in the Human Brain. Anatomia 2024, 3 (2), 68–92. https://doi.org/10.3390/anatomia3020007.
  • Gholivand, A.; Korculanin, O.; Dahlhoff, K.; Babaki, M.; Dickscheid, T.; Lettinga, M. P. Effect of In-Plane and out-of-Plane Bifurcated Microfluidic Channels on the Flow of Aggregating Red Blood Cells. Lab Chip 2024. https://doi.org/10.1039/D4LC00151F.
  • Eberhard, D.; Balkenhol, S.; Köster, A.; Follert, P.; Upschulte, E.; Ostermann, P.; Kirschner, P.; Uhlemeyer, C.; Charnay, I.; Preuss, C.; Trenkamp, S.; Belgardt, B.-F.; Dickscheid, T.; Esposito, I.; Roden, M.; Lammert, E. Semaphorin-3A Regulates Liver Sinusoidal Endothelial Cell Porosity and Promotes Hepatic Steatosis. Nat Cardiovasc Res 2024, 3 (6), 734–753. https://doi.org/10.1038/s44161-024-00487-z.
  • Amunts, K.; Axer, M.; Banerjee, S.; Bitsch, L.; Bjaalie, J. G.; Brauner, P.; Brovelli, A.; Calarco, N.; Carrere, M.; Caspers, S.; Charvet, C. J.; Cichon, S.; Cools, R.; Costantini, I.; D’Angelo, E. U.; De Bonis, G.; Deco, G.; DeFelipe, J.; Destexhe, A.; Dickscheid, T.; Diesmann, M.; Düzel, E.; Eickhoff, S. B.; Einevoll, G.; Eke, D.; Engel, A. K.; Evans, A. C.; Evers, K.; Fedorchenko, N.; Forkel, S. J.; Fousek, J.; Friederici, A. D.; Friston, K.; Furber, S.; Geris, L.; Goebel, R.; Güntürkün, O.; Hamid, A. I. A.; Herold, C.; Hilgetag, C. C.; Hölter, S. M.; Ioannidis, Y.; Jirsa, V.; Kashyap, S.; Kasper, B. S.; d’Exaerde, A. D. K.; Kooijmans, R.; Koren, I.; Kotaleski, J. H.; Kiar, G.; Klijn, W.; Klüver, L.; Knoll, A. C.; Krsnik, Z.; Kämpfer, J.; Larkum, M. E.; Linne, M.-L.; Lippert, T.; Abdullah, J. M.; Maio, P. D.; Magielse, N.; Maquet, P.; Mascaro, A. L. A.; Marinazzo, D.; Mejias, J.; Meyer-Lindenberg, A.; Migliore, M.; Michael, J.; Morel, Y.; Morin, F. O.; Muckli, L.; Nagels, G.; Oden, L.; Palomero-Gallagher, N.; Panagiotaropoulos, F.; Paolucci, P. S.; Pennartz, C.; Peeters, L. M.; Petkoski, S.; Petkov, N.; Petro, L. S.; Petrovici, M. A.; Pezzulo, G.; Roelfsema, P.; Ris, L.; Ritter, P.; Rockland, K.; Rotter, S.; Rowald, A.; Ruland, S.; Ryvlin, P.; Salles, A.; Sanchez-Vives, M. V.; Schemmel, J.; Senn, W.; De Sousa, A. A.; Ströckens, F.; Thirion, B.; Uludağ, K.; Vanni, S.; Van Albada, S. J.; Vanduffel, W.; Vezoli, J.; Vincenz-Donnelly, L.; Walter, F.; Zaborszky, L. The Coming Decade of Digital Brain Research: A Vision for Neuroscience at the Intersection of Technology and Computing. Imaging Neuroscience 2024, 2, 1–35. https://doi.org/10.1162/imag_a_00137.
  • Zachlod, D.; Palomero-Gallagher, N.; Dickscheid, T.; Amunts, K. Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity. Biological Psychiatry 2023, 93 (5), 471–479. https://doi.org/10.1016/j.biopsych.2022.09.014.
  • Upschulte, E.; Harmeling, S.; Amunts, K.; Dickscheid, T. Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images. 2023.
  • Unger, N.; Haeck, M.; Eickhoff, S. B.; Camilleri, J. A.; Dickscheid, T.; Mohlberg, H.; Bludau, S.; Caspers, S.; Amunts, K. Cytoarchitectonic Mapping of the Human Frontal Operculum—New Correlates for a Variety of Brain Functions. Front. Hum. Neurosci. 2023, 17, 1087026. https://doi.org/10.3389/fnhum.2023.1087026.
  • Sip, V.; Hashemi, M.; Dickscheid, T.; Amunts, K.; Petkoski, S.; Jirsa, V. Characterization of Regional Differences in Resting-State fMRI with a Data-Driven Network Model of Brain Dynamics. Sci. Adv. 2023, 9 (11), eabq7547. https://doi.org/10.1126/sciadv.abq7547.
  • Kleven, H.; Gillespie, T. H.; Zehl, L.; Dickscheid, T.; Bjaalie, J. G.; Martone, M. E.; Leergaard, T. B. AtOM, an Ontology Model to Standardize Use of Brain Atlases in Tools, Workflows, and Data Infrastructures. Sci Data 2023, 10 (1), 486. https://doi.org/10.1038/s41597-023-02389-4.
  • Van Albada, S. J.; Morales-Gregorio, A.; Dickscheid, T.; Goulas, A.; Bakker, R.; Bludau, S.; Palm, G.; Hilgetag, C.-C.; Diesmann, M. Bringing Anatomical Information into Neuronal Network Models. In Computational Modelling of the Brain; Giugliano, M., Negrello, M., Linaro, D., Eds.; Advances in Experimental Medicine and Biology; Springer International Publishing: Cham, 2022; Vol. 1359, pp 201–234. https://doi.org/10.1007/978-3-030-89439-9_9.
  • Vaca, E.; Menzel, M.; Amunts, K.; Axer, M.; Dickscheid, T. GORDA: Graph-Based Orientation Distribution Analysis of SLI Scatterometry Patterns of Nerve Fibres. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI); 2022; pp 1–5. https://doi.org/10.1109/ISBI52829.2022.9761492.
  • Upschulte, E.; Harmeling, S.; Amunts, K.; Dickscheid, T. Contour Proposal Networks for Biomedical Instance Segmentation. Medical Image Analysis 2022, 102371. https://doi.org/10.1016/j.media.2022.102371.
  • Schirner, M.; Domide, L.; Perdikis, D.; Triebkorn, P.; Stefanovski, L.; Pai, R.; Prodan, P.; Valean, B.; Palmer, J.; Langford, C.; Blickensdörfer, A.; van der Vlag, M.; Diaz-Pier, S.; Peyser, A.; Klijn, W.; Pleiter, D.; Nahm, A.; Schmid, O.; Woodman, M.; Zehl, L.; Fousek, J.; Petkoski, S.; Kusch, L.; Hashemi, M.; Marinazzo, D.; Mangin, J.-F.; Flöel, A.; Akintoye, S.; Stahl, B. C.; Cepic, M.; Johnson, E.; Deco, G.; McIntosh, A. R.; Hilgetag, C. C.; Morgan, M.; Schuller, B.; Upton, A.; McMurtrie, C.; Dickscheid, T.; Bjaalie, J. G.; Amunts, K.; Mersmann, J.; Jirsa, V.; Ritter, P. Brain Simulation as a Cloud Service: The Virtual Brain on EBRAINS. NeuroImage 2022, 251, 118973. https://doi.org/10.1016/j.neuroimage.2022.118973.
  • Quabs, J.; Caspers, S.; Schöne, C.; Mohlberg, H.; Bludau, S.; Dickscheid, T.; Amunts, K. Cytoarchitecture, Probability Maps and Segregation of the Human Insula. NeuroImage 2022, 260, 119453. https://doi.org/10.1016/j.neuroimage.2022.119453.
  • Schiffer, C.; Spitzer, H.; Kiwitz, K.; Unger, N.; Wagstyl, K.; Evans, A. C.; Harmeling, S.; Amunts, K.; Dickscheid, T. Convolutional Neural Networks for Cytoarchitectonic Brain Mapping at Large Scale. NeuroImage 2021, 240, 118327. https://doi.org/10.1016/j.neuroimage.2021.118327.
  • Schiffer, C.; Harmeling, S.; Amunts, K.; Dickscheid, T. 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; de Bruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, 2021; pp 395–404. https://doi.org/10.1007/978-3-030-87237-3_38.
  • Schiffer, C.; Amunts, K.; Harmeling, S.; Dickscheid, T. Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI); 2021; pp 603–606. https://doi.org/10.1109/ISBI48211.2021.9433986.
  • Paquola, C.; Royer, J.; Lewis, L. B.; Lepage, C.; Glatard, T.; Wagstyl, K.; DeKraker, J.; Toussaint, P.-J.; Valk, S. L.; Collins, D. L.; Khan, A.; Amunts, K.; Evans, A. C.; Dickscheid, T.; Bernhardt, B. C. The BigBrainWarp Toolbox for Integration of BigBrain 3D Histology with Multimodal Neuroimaging. eLife 2021, 10, e70119. https://doi.org/10.7554/eLife.70119.
  • Wagstyl, K.; Larocque, S.; Cucurull, G.; Lepage, C.; Cohen, J. P.; Bludau, S.; Palomero-Gallagher, N.; Lewis, L. B.; Funck, T.; Spitzer, H.; Dickscheid, T.; Fletcher, P. C.; Romero, A.; Zilles, K.; Amunts, K.; Bengio, Y.; Evans, A. C. BigBrain 3D Atlas of Cortical Layers: Cortical and Laminar Thickness Gradients Diverge in Sensory and Motor Cortices. PLoS biology 2020, 18 (4), e3000678. https://doi.org/10.1371/journal.pbio.3000678.
  • Kiwitz, K.; Schiffer, C.; Spitzer, H.; Dickscheid, T.; Amunts, K. Deep Learning Networks Reflect Cytoarchitectonic Features Used in Brain Mapping. Scientific Reports 2020, 10 (1), 1–15. https://doi.org/10.1038/s41598-020-78638-y.
  • Oden, L.; Schiffer, C.; Spitzer, H.; Dickscheid, T.; Pleiter, D. 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); 2019; pp 291–298. https://doi.org/10.1109/EMPDP.2019.8671630.
  • Huysegoms, M.; Haas, S.; Bludau, S.; Amunts, K.; Dickscheid, T. Aligning Images of Large Human Brain Sections on a Cellular Level Using Bisected Cells. In 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM); Rome, 2019.
  • Dickscheid, T.; Haas, S.; Bludau, S.; Glock, P.; Huysegoms, M.; Amunts, K. Towards 3D Reconstruction of Neuronal Cell Distributions from Histological Human Brain Sections. Future Trends of HPC in a Disruptive Scenario 2019, 223–239. https://doi.org/10.3233/APC190016.
  • Spitzer, H.; Kiwitz, K.; Amunts, K.; Harmeling, S.; Dickscheid, T. Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-Supervised Siamese Networks. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C., Fichtinger, G., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, 2018; pp 663–671. https://doi.org/10.1007/978-3-030-00931-1_76.
  • Spitzer, H.; Amunts, K.; Harmeling, S.; Dickscheid, T. Compact Feature Representations for Human Brain Cytoarchitecture Using Self-Supervised Learning. In International conference on Medical Imaging with Deep Learning; 2018.
  • Lebenberg, J.; Labit, M.; Auzias, G.; Mohlberg, H.; Fischer, C.; Rivière, D.; Duchesnay, E.; Kabdebon, C.; Leroy, F.; Labra, N.; Poupon, F.; Dickscheid, T.; Hertz-Pannier, L.; Poupon, C.; Dehaene-Lambertz, G.; Hüppi, P.; Amunts, K.; Dubois, J.; Mangin, J.-F. A Framework Based on Sulcal Constraints to Align Preterm, Infant and Adult Human Brain Images Acquired in Vivo and Post Mortem. Brain Struct Funct 2018, 223 (9), 4153–4168. https://doi.org/10.1007/s00429-018-1735-9.
  • Spitzer, H.; Amunts, K.; Harmeling, S.; Dickscheid, T. 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); 2017; pp 920–923. https://doi.org/10.1109/ISBI.2017.7950666.
  • Schober, M.; Axer, M.; Huysegoms, M.; Schubert, N.; Amunts, K.; Dickscheid, T. Morphing Image Masks for Stacked Histological Sections Using Laplace’s Equation. In Bildverarbeitung für die Medizin 2016; Tolxdorff, T., Deserno, T. M., Handels, H., Meinzer, H.-P., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2016; pp 146–151.
  • Mangin, J.-F.; Lebenberg, J.; Lefranc, S.; Labra, N.; Auzias, G.; Labit, M.; Guevara, M. A.; Mohlberg, H.; Roca, P.; Guevara, P.; Dubois, J.; Leroy, F.; Dehaene-Lambertz, G.; Cachia, A.; Dickscheid, T.; Coulon, O.; Poupon, C.; Rivière, D.; Amunts, K.; Sun, Z. Y. Spatial Normalization of Brain Images and Beyond. Medical Image Analysis 2016, 33, 127–133. https://doi.org/10.1016/j.media.2016.06.008.
  • Pieperhoff, P.; Dickscheid, T.; Amunts, K. Grundlagen Der Morphometrie. In Funktionelle MRT in Psychiatrie und Neurologie; Schneider, F., Fink, G. R., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013; pp 87–101. https://doi.org/10.1007/978-3-642-29800-4_5.
  • Dickscheid, T.; Förstner, W. A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships. Photogrammetrie - Fernerkundung - Geoinformation 2013, 2013 (4), 269–283. https://doi.org/10.1127/1432-8364/2013/0176.
  • Amunts, K.; Lepage, C.; Borgeat, L.; Mohlberg, H.; Dickscheid, T.; Rousseau, M.-E.; Bludau, S.; Bazin, P.-L.; Lewis, L. B.; Oros-Peusquens, A.-M.; Shah, N. J.; Lippert, T.; Zilles, K.; Evans, A. C. BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science 2013, 340 (6139), 1472–1475. https://doi.org/10.1126/science.1235381.
  • Kleiner, M.; Axer, M.; Gräßel, D.; Reckfort, J.; Pietrzyk, U.; Amunts, K.; Dickscheid, T. Classification of Ambiguous Nerve Fiber Orientations in 3D Polarized Light Imaging. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012; Ayache, N., Delingette, H., Golland, P., Mori, K., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2012; pp 206–213. https://doi.org/10.1007/978-3-642-33415-3_26.
  • Dammers, J.; Breuer, L.; Axer, M.; Kleiner, M.; Eiben, B.; Gräßel, D.; Dickscheid, T.; Zilles, K.; Amunts, K.; Shah, N. J.; Pietrzyk, U. Automatic Identification of Gray and White Matter Components in Polarized Light Imaging. NeuroImage 2012, 59 (2), 1338–1347. https://doi.org/10.1016/j.neuroimage.2011.08.030.
  • Dickscheid, T.; Schindler, F.; Förstner, W. Coding Images with Local Features. International Journal of Computer Vision 2011, 94 (2), 154–174. https://doi.org/10.1007/s11263-010-0340-z.
  • Axer, M.; Grässel, D.; Kleiner, M.; Dammers, J.; Dickscheid, T.; Reckfort, J.; Hütz, T.; Eiben, B.; Pietrzyk, U.; Zilles, K.; Amunts, K. High-Resolution Fiber Tract Reconstruction in the Human Brain by Means of Three-Dimensional Polarized Light Imaging. Front. Neuroinform. 2011, 5. https://doi.org/10.3389/fninf.2011.00034.
  • Förstner, W.; Dickscheid, T.; Schindler, F. On the Completeness of Coding with Image Features. In Procedings of the British Machine Vision Conference 2009; British Machine Vision Association, 2009. https://doi.org/10.5244/c.23.1.
  • Förstner, W.; Dickscheid, T.; Schindler, F. Detecting Interpretable and Accurate Scale-Invariant Keypoints. In 2009 IEEE 12th International Conference on Computer Vision; 2009; pp 2256–2263. https://doi.org/10.1109/ICCV.2009.5459458.
  • Dickscheid, T.; Förstner, W. Evaluating the Suitability of Feature Detectors for Automatic Image Orientation Systems. In Computer Vision Systems; Fritz, M., Schiele, B., Piater, J. H., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2009; pp 305–314.
  • Läbe, T.; Dickscheid, T.; Förstner, W. On the Quality of Automatic Relative Orientation Procedures. In 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS); Beijing, China, 2008; pp 37-42 Part B3b-1.
  • Dickscheid, T.; Läbe, T.; Förstner, W. Benchmarking Automatic Bundle Adjustment Results. In 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS); Beijing, China, 2008; pp 7–12, Part B3a.
  • Schmidt, C.; Tavernier, L.; Dickscheid, T.; Paulus, D. Computer Analysis of Ornaments. In Electronic Imaging & the Visual Arts; Cappellini, V., Hemsley, J., Eds.; Pitagora Editrice Bologna: Florence, 2005; pp 129–134.
  • Paulus, D.; Dickscheid, T.; Berg, K. . Design of AN IMage AnaLysis System. In Seventh International Workshop on Computer Architecture for Machine Perception (CAMP’05); 2005; pp 147–152. https://doi.org/10.1109/CAMP.2005.20.