Tim Scherr, M.Sc.

Tim Scherr, M.Sc.

  • Karlsruhe Institute of Technology (KIT)
    Institute for Automation and Applied Informatics (IAI)

    Hermann-von-Helmholtz-Platz 1
    76344 Eggenstein-Leopoldshafen

    Fax: +49 721 608 22602
    Building-No.: 445 / 449 / 668

Publications


2021
A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure.
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, November 8, 2021
2020
BeadNet: Deep learning-based bead detection and counting in low-resolution microscopy images.
Scherr, T.; Streule, K.; Bartschat, A.; Böhland, M.; Stegmaier, J.; Reischl, M.; Orian-Rousseau, V.; Mikut, R.
2020. Bioinformatics, 36 (17), 4668–4670. doi:10.1093/bioinformatics/btaa594
Pax6 organizes the anterior eye segment by guiding two distinct neural crest waves.
Takamiya, M.; Stegmaier, J.; Kobitski, A. Y.; Schott, B.; Weger, B. D.; Margariti, D.; Cereceda Delgado, A. R.; Gourain, V.; Scherr, T.; Yang, L.; Sorge, S.; Otte, J. C.; Hartmann, V.; Wezel, J. van; Stotzka, R.; Reinhard, T.; Schlunck, G.; Dickmeis, T.; Rastegar, S.; Mikut, R.; Nienhaus, G. U.; Strähle, U.
2020. PLoS Genetics, 16 (6), Art. Nr.: e1008774. doi:10.1371/journal.pgen.1008774
Segregation of Dispersed Silica Nanoparticles in Microfluidic Water‐in‐Oil Droplets: A Kinetic Study.
Sheshachala, S.; Grösche, M.; Scherr, T.; Hu, Y.; Sun, P.; Bartschat, A.; Mikut, R.; Niemeyer, C. M.
2020. ChemPhysChem, 21 (10), 1070–1078. doi:10.1002/cphc.201901151
Submission of KIT-LS-GE.
Löffler, K.; Scherr, T.
2020, April 3. Workshop : ISBI Cell Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI 2020), Iowa City, IA, USA, April 3–7, 2020
Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.
Scherr, T.; Löffler, K.; Böhland, M.; Mikut, R.
2020. PLOS ONE, 15 (12), Art. Nr.: e0243219. doi:10.1371/journal.pone.0243219
2019
Fuzzy tissue detection for real-time focal control in corneal confocal microscopy = Fuzzy-Gewebeerkennung für Echtzeit-Fokusregelung in der Kornea-Konfokalmikroskopie.
Bartschat, A.; Allgeier, S.; Scherr, T.; Stegmaier, J.; Bohn, S.; Reichert, K.-M.; Kuijper, A.; Reischl, M.; Stachs, O.; Köhler, B.; Mikut, R.
2019. Automatisierungstechnik, 67 (10), 879–888. doi:10.1515/auto-2019-0034
Digitale Bildverarbeitung und Tiefe Neuronale Netze in der Augenheilkunde – aktuelle Trends - Digital Image Processing and Deep Neural Networks in Ophthalmology – Current Trends.
Bartschat, A.; Allgeier, S.; Bohn, S.; Scherr, T.; Blessing, D.; Reichert, K.-M.; Reischl, M.; Stachs, O.; Koehler, B.; Mikut, R.
2019. Klinische Monatsblätter für Augenheilkunde, 236 (12), 1399–1406. doi:10.1055/a-1008-9400
Influence of Synthetic Label Image Object Properties on GAN Supported Segmentation Pipelines.
Böhland, M.; Scherr, T.; Bartschat, A.; Mikut, R.; Reischl, M.
2019. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. Ed.: F. Hoffmann, E. Hüllermeier, R. Mikut, 289–309, KIT Scientific Publishing
2018
Best Practices in Deep Learning-Based Segmentation of Microscopy Images.
Scherr, T.; Bartschat, A.; Reischl, M.; Stegmaier, J.; Mikut, R.
2018. Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018. Ed.: F. Hoffmann, 175–195, KIT Scientific Publishing. doi:10.5445/IR/1000087734
Deep learning approaches to improve cell segmentation and tracking accuracy for interactive knowledge discovery in Zebrafish embryos.
Scherr, T.; Schott, B.; Traub, M.; Takamiya, M.; Bartschat, A.; Kobitski, A.; Nienhaus, G. U.; Strähle U.; Mikut, R.; Stegmaier, J.
2018. Zebrafish Models for Human Eye Diseases, Freiburg, September 14-15, 2018
Interactive analysis of cell tracks in light sheet microscopy images using embryominer.
Scherr, T.; Schott, B.; Traub, M.; Takamiya, M.; Bartschat, A.; Kobitski, A. Y.; Nienhaus, G. U.; Strähle, U.; Stegmaier, J.; Mikut, R.
2018. 10th Light Sheet Fluorescence Microscopy Conference (LSFM 2018), Dresden, Germany, August 12–15, 2018
Robustness of Deep Learning Architectures with Respect to Training Data Variation.
Bartschat, A.; Unger, T.; Scherr, T.; Stegmaier, J.; Mikut, R.; Reischl, M.
2018. Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018. Ed.: F. Hoffmann, 129–138, KIT Scientific Publishing. doi:10.5445/IR/1000087724