Andreas Bartschat, 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
Burst of corneal dendritic cells during Trastuzumab and Paclitaxel treatment.
Sterenczak, K. A.; Stache, N.; Bohn, S.; Allgeier, S.; Köhler, B.; Bartschat, A.; George, C.; Guthoff, R. F.; Stachs, O.; Stachs, A.
2021. Diagnostics, 11 (5), 838. doi:10.3390/diagnostics11050838
Parkinson’s disease with restless legs syndrome - an in vivo corneal confocal microscopy study.
Andréasson, M.; Lagali, N.; Badian, R. A.; Utheim, T. P.; Scarpa, F.; Colonna, A.; Allgeier, S.; Bartschat, A.; Köhler, B.; Mikut, R.; Reichert, K.-M.; Solders, G.; Samuelsson, K.; Zetterberg, H.; Blennow, K.; Svenningsson, P.
2021. npj Parkinson’s Disease, 7 (1), Art.-Nr. 4. doi:10.1038/s41531-020-00148-5
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
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
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
Motion prediction enables simulated MR-imaging of freely moving model organisms.
Reischl, M.; Jouda, M.; MacKinnon, N.; Fuhrer, E.; Bakhtina, N.; Bartschat, A.; Mikut, R.; Korvink, J. G.
2019. PLoS Computational Biology, 15 (12), e1006997. doi:10.1371/journal.pcbi.1006997
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
Applanation force monitoring during in vivo corneal confocal laser scanning microscopy.
Bohn, S.; Sperlich, K.; Bartschat, A.; Stolu, H.; Guthoff, R. R.; Mikut, R.; Köhler, B.; Stacks, O.
2019. Der Ophthalmologe, 116 (Suppl.2), S139
Evaluation of Features for Change Detection in Unstructured Image Data.
Münke, F. R.; Bartschat, A.; Chen, Y.; Mikut, R.; Reischl, M.
2019. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. Ed.: F. Hoffmann, E. Hüllermeier, R. Mikut, 1–23, KIT Scientific Publishing
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
Applanation force monitoring during in vivo corneal confocal laser scanning microscopy.
Bohn, S.; Sperlich, K.; Bartschat, A.; Stolu, H.; Guthoff, R. R.; Mikut, R.; Köhler, B.; Stacks, O.
2019. 117. DOG-Kongress (DOG 2019), Berlin, Germany, September 26–29, 2019
Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization.
Chaussonnet, G.; Lieber, C.; Yikang, Y.; Gu, W.; Bartschat, A.; Reischl, M.; Koch, R.; Mikut, R.; Bauer, H.-J.
2019. doi:10.5445/IR/1000097897/v3
Automated Classification of Fertilized Zebrafish Embryos.
Neukum, A.; Bartschat, A.; Breitwieser, H.; Strähle, U.; Dickmeis, T.; Pylatiuk, C.
2019. Zebrafish, 16 (3), 326–328. doi:10.1089/zeb.2019.1728
Data mining tools.
Bartschat, A.; Reischl, M.; Mikut, R.
2019. Wiley interdisciplinary reviews / Data mining and knowledge discovery, 9 (4), Article: e1309. doi:10.1002/widm.1309
2018
3D confocal laser-scanning microscopy for large-area imaging of the corneal subbasal nerve plexus.
Allgeier, S.; Bartschat, A.; Bohn, S.; Peschel, S.; Reichert, K.-M.; Sperlich, K.; Walckling, M.; Hagenmeyer, V.; Mikut, R.; Stachs, O.; Köhler, B.
2018. Scientific reports, 8 (1), Article Number 7468. doi:10.1038/s41598-018-25915-6
Analysis of focus shift speed for in vivo 3D corneal confocal microscopy.
Allgeier, S.; Bartschat, A.; Bohn, S.; Reichert, K.-M.; Sperlich, K.; Mikut, R.; Stachs, O.; Köhler, B.
2018. Biomedical engineering, 63 (s1), S192. doi:10.1515/bmt-2018-6037
Cellular in vivo 3D imaging of the cornea by confocal laser scanning microscopy.
Bohn, S.; Sperlich, K.; Allgeier, S.; Bartschat, A.; Prakasam, R.; Reichert, K.-M.; Stolz, H.; Guthoff, R.; Mikut, R.; Köhler, B.; Stachs, O.
2018. Biomedical optics express, 9 (6), 2511–2525. doi:10.1364/BOE.9.002511
EmbryoMiner : A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.
Schott, B.; Traub, M.; Schlagenhauf, C.; Takamiya, M.; Antritter, T.; Bartschat, A.; Löffler, K.; Blessing, D.; Otte, J. C.; Kobitski, A. Y.; Nienhaus, G. U.; Strähle, U.; Mikut, R.; Stegmaier, J.
2018. PLoS Computational Biology, 14 (4), e1006128. doi:10.1371/journal.pcbi.1006128
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
Slit lamp microscopy on a cellular level using in vivo confocal laser scanning microscopy.
Bohn, S.; Sperlich, K.; Kala Praksam, R.; Allgeier, S.; Reichert, K.-M.; Bartschat, A.; Stolz, H.; Guthoff, R. F.; Köhler, B.; Mikut, R.; Stachs, O.
2018. Investigative ophthalmology & visual science, 59 (9), 3439
Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection.
Stegmaier, J.; Spina, T. V.; Falcao, A. X.; Bartschat, A.; Mikut, R.; Meyerowitz, E.; Cunha, A.
2018. 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018; Washington; United States; 4 April 2018 through 7 April 2018, 382–386, IEEE Computer Society. doi:10.1109/ISBI.2018.8363598
Concept and benchmark results for Big Data energy forecasting based on Apache Spark.
González Ordiano, J. Á.; Bartschat, A.; Ludwig, N.; Braun, E.; Waczowicz, S.; Renkamp, N.; Peter, N.; Düpmeier, C.; Mikut, R.; Hagenmeyer, V.
2018. Journal of Big Data, 5 (1), Art.Nr. 11. doi:10.1186/s40537-018-0119-6
2017
ZebrafishMiner: an open source software for interactive evaluation of domain-specific fluorescence in zebrafish.
Reischl, M.; Bartschat, A.; Liebel, U.; Gehrig, J.; Müller, F.; Mikut, R.
2017. Current directions in biomedical engineering, 3 (2), 199–202. doi:10.1515/cdbme-2017-0042
Augmentations of the Bag of Visual Words Approach for Real-Time Fuzzy and Partial Image Classification.
Bartschat, A.; Stegmaier, J.; Allgeier, S.; Reichert, K.-M.; Bohn, S.; Stachs, O.; Köhler, B.; Mikut, R.
2017. Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. Hrsg.: F. Hoffmann, 227–242, KIT Scientific Publishing
Concepts for automated fast focal plane control in subbasal nerve plexus mosaicking to reliably quantify a biomarker for diabetic peripheral neuropathy.
Bohn, S.; Allgeier, S.; Bartschat, A.; Guthoff, R.; Köhler, B.; Mikut, R.; Reichert, K.-M.; Sperlich, K.; Stolz, H.; Stachs, O.
2017. Investigative ophthalmology & visual science, 58 (8), Art. Nr.: 1431
Concepts for automated fast focal plane control in subbasal nerve plexus mosaicking to reliably quantify a biomarker for diabetic peripheral neuropathy.
Bohn, S.; Allgeier, S.; Bartschat, A.; Guthoff, R.; Köhler, B.; Mikut, R.; Reichert, K.-M.; Sperlich, K.; Stolz, H.; Stachs, O.
2017. ARVO Annual Meeting, Baltimore, MD, May 7-11, 2017
In-vivo-Bildgebung des kornealen Nervenplexus : Vom Einzelbild zur großflächigen Darstellung.
Köhler, B.; Allgeier, S.; Bartschat, A.; Guthoff, R. F.; Bohn, S.; Reichert, K.-M.; Stachs, O.; Winter, K.; Mikut, R.
2017. Der Ophthalmologe, 114 (7), 601–607. doi:10.1007/s00347-017-0464-4
2016
Automatic corneal tissue classification using bag-of-visual-words approaches.
Bartschat, A.; Toso, L.; Stegmaier, J.; Kuijper, A.; Mikut, R.; Köhler, B.; Allgeier, S.
2016. Forum Bildverarbeitung 2016. Hrsg.: M. Heizmann, 245–256, KIT Scientific Publishing
XPIWIT : An XML pipeline wrapper for the Insight Toolkit.
Bartschat, A.; Hübner, E.; Reischl, M.; Mikut, R.; Stegmaier, J.
2016. Bioinformatics, 32 (2), 315–317. doi:10.1093/bioinformatics/btv559
2014
Efficient extraction of cell shapes and nuclei from 3D light-sheet microscopy images.
Stegmaier, J.; Amat, F.; Takamiya, M.; Bartschat, A.; Otte, J. C.; Kobitski, A.; Lemon, B.; McDole, K.; Wan, Y.; Nienhaus, G. U.; Strähle, U.; Keller, P. J.; Mikut, R.
2014. BioImage Informatics Meeting, Leuven, B, October 8-10, 2014
Fast Segmentation of Stained Nuclei in Terabyte-Scale, Time Resolved 3D Microscopy Image Stacks.
Stegmaier, J.; Otte, J. C.; Kobitski, A.; Bartschat, A.; Garcia, A.; Nienhaus, G. U.; Strähle, U.; Mikut, R.
2014. PLoS ONE, 9 (2), e90036/1–11. doi:10.1371/journal.pone.0090036
2012
A prototyping environment for evaluation of man-machine interfaces based on electromyographic activity.
Bartschat, A.; Rupp, R.; Liebetanz, D.; Meinke, J.; Klinker, F.; Hewitt, M.; Reischl, M.
2012. Biomedizinische Technik, 57, 1095. doi:10.1515/bmt-2012-4243
A prototyping environment for evaluation of man-machine interfaces based on electromyographic activity.
Bartschat, A.; Rupp, R.; Liebetanz, D.; Meinke, J.; Klinker, F.; Hewitt, M.; Reischl, M.
2012. BMT 2012 : 46.Jahrestagung der Deutschen Gesellschaft für Biomedizinische Technik (DGBMT), Jena, 16.-19.September 2012
On the quantification of tissue fluorescence in zebrafish.
Reischl, M.; Bartschat, A.; Eberle, F.; Gehrig, J.; Liebel, U.; Mueller, F.; Mikut, R.
2012. 2nd European Zebrafish PI Meeting (EZPM 2012), Karlsruhe, March 21-23, 2012
On the quantification of tissue fluorescence in zebrafish.
Reischl, M.; Bartschat, A.; Eberle, F.; Gehrig, J.; Liebel, U.; Mueller, F.; Mikut, R.
2012. 2nd European Zebrafish PI Meeting (EZPM 2012), Karlsruhe, March 21-23, 2012