Prof. Dr. Benjamin Risse

Prof. Dr. Benjamin Risse

Einsteinstraße 62, Raum 609
48149 Münster

T: +49 251 83-32717
F: +49 251 83-33 755

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Akademische Profile

  • Forschungsschwerpunkte

    Computer Vision

    Computer Vision; Image Processing; Computer Graphics

    Machine Learning

    Deep Learning; Pattern Recognition

    Engineering

    3D Printing; Robotics; Embedded Systems; Imaging Techniques; Sensor Fusion

    Biomedicine

    Behavioural Biology; Neurobiology; Artificial Life

  • Weitere Zugehörigkeiten an der Universität Münster

  • Vita

    Akademische Ausbildung

    Professor, University of Münster, Münster, Germany
    Junior Professor (W1), University of Münster, Münster, Germany
    Ph. D. studies, University of Münster, Münster Germany
    Diplom Informatiker (Dipl. Inf.; MSc equivalent), University of Münster, Münster, Germany

    Beruflicher Werdegang

    Professor, University of Münster, Münster, Germany
    Junior Professor (W1), University of Münster, Münster, Germany, Faculty of Mathematics and Computer Science
    Research Associate, University of Edinburgh, Edinburgh, United Kingdom, Institute for Action, Perception and Behaviour (Insect Robotics Group)
    Ph. D. studies, University of Münster, Münster, Germany, Supervisors: Prof. X. Jiang (Pattern Recognition and Image Analysis) & Prof. C. Klämbt (Institute for Neuro- and Behavioural Biology)
    Diploma degree in computer science with a minor in biology, University of Münster, Münster, Germany

    Preise

    Lehrpreis – Universität Münster
    Lehrpreis – Fachschaft für Mathematik & Informatik der Universität Münster
    BMVC Outstanding Reviewer Award – The British Machine Vision Association (BMVC)
    Preis für Praktische Informatik (1. Platz) – IHK Nord Westfalen

    Mitgliedschaften und Aktivitäten in Gremien

    German Informatics Society Membership, Gesellschaft für Informatik e.V. (GI)
    IEEE Membership, Young Professionals and Computer Society Membership.

    Rufe

    wwu muenster, Geoinformatics (W2) – angenommen
    wwu muenster, Praktische Informatik (W1) – angenommen
  • Lehre

    Vorlesungen
    Seminar
    Praktikum

    Vorlesung
    Seminar

    Vorlesung
    Seminare
    Praktikum
    Kolloquium

    Vorlesung
    Seminare
    Sonstige Lehrveranstaltung

    Übung
    Kolloquium
    Sonstige Lehrveranstaltung
  • Projekte

    • InFlame – Else Kröner Medical Scientist Kolleg Münster - Dynamik von Entzündungsreaktionen ()
      participations in other joint project: Else Kröner Medical Scientist Kolleg | Förderkennzeichen: 2021_EKMK.13
    • SPP 2363 - Teilprojekt: Neuronale Fingerabdrücke als struktur- und aktivitätssensitive molekulare Darstellungen ()
      Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Schwerpunktprogramm | Förderkennzeichen: KO 4689/7-1; RI 2938/3-1
    • InterKIWWU – Interdisziplinäres Lehrprogramm zu maschinellem Lernen und künstlicher Intelligenz ()
      Gefördertes Einzelprojekt: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 16DHBKI049
    • maQinto – Maschinell trainierter Qualitätssensor, intelligente Prozessteuerung und ein ML-Framework zur ressourceneffizienten, maßgeschneiderten Kohlenstofffaserherstellung ()
      participations in bmbf-joint project: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 01I522020D
    • SFB 1450 B04 - Multiskalige Darstellung und Analyse der Leukozytenwanderung in hypoxischen Entzündungsbereichen in vivo ()
      Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Sonderforschungsbereich | Förderkennzeichen: SFB 1450/1, B04
    • SFB 1459 C05 - Kohärente nanophotonische neuronale Netzwerke mit adaptiven molekularen Systemen ()
      Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Sonderforschungsbereich | Förderkennzeichen: SFB 1459/1, C05
    • meditrain – Verbundprojekt: Intelligente Virtuelle Agenten für die Medizinische Ausbildung (medical tr.AI.ning) - Teilvorhaben: Entwicklung und Erforschung von intelligenten virtuellen Agenten für die klinisch-medizinische Ausbildung mit Schwerpunkt in KI ()
      participations in bmbf-joint project: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 16DHBKI077
    • Friends with benefits? A holistic approach to diffuse mutualism in plant pollinator interactions ()
      participations in other joint project: HFSP - Research Grant - Program | Förderkennzeichen: RGP0057/2021
    • Learning from Neuroscience to Investigate the IQ of Deep Neural Networks ()
      Gefördertes Einzelprojekt: MKW - Förderlinie „Künstliche Intelligenz/Maschinelles Lernen“ - KI-Starter | Förderkennzeichen: 005-2010-0055
    • GIGA Gebärdensprachen - Entwicklung einer 5G enabled Gebärdensprachen-Applikation ()
      participations in other joint project: MWIKE NRW - Förderwettbewerb 5G.NRW | Förderkennzeichen: FKZ 005-2018-0104; 005-2108-0102; PtJ-Nr. 2008gif042e; 2108gif042c
    • Augmented and Virtual Reality in der medizinischen Ausbildung ()
      Gefördertes Einzelprojekt: Institut für Ausbildung und Studienangelegenheiten der Medizinischen Fakultät
    • SMARTPRINT – KMU-innovativ Verbundprojekt: Entwicklung eines Intelligenten 3D Druckers (SMARTPRINT); Teilprojekt: Erforschung und Entwicklung der Kl-Algorithmen ()
      Gefördertes Einzelprojekt: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 02P19K121
    • QuBe – QuBe (Tools for Quantitative Behaviour) - Investitionen in Wachstum und Beschäftigung ()
      Gefördertes Einzelprojekt: MWIKE NRW - EFRE/JTF-Programm - EFRE Start-up Transfer.NRW - START-UP-Hochschul-Ausgründungen NRW | Förderkennzeichen: EFRE-0400299
    • Reproduction – from Genes to Molecules and Function ()
      Durch die Universität Münster intern gefördertes Projekt: Universität Münster-interne Förderung - Topical Programs
    • Das Individuum im Fokus der Lebenswissenschaften ()
      Durch die Universität Münster intern gefördertes Projekt: Universität Münster-interne Förderung - Topical Programs
    • EIMD – EMID - Electron Microscopy Imaging in the Dark - Entwicklung der Nachbelichtungsfunktionalität des EMID Verfahrens ()
      participations in other joint project: BMWK - Zentrales Innovationsprogramm Mittelstand | Förderkennzeichen: ZF4649501TS8
    • Artificial Intelligence for Additive Manufacturing ()
      Gefördertes Einzelprojekt: tapdo technologies GmbH
    • Entwicklung eines Verfahrens zur automatischen Erstellung von Reitsportaufnahmen ()
      Gefördertes Einzelprojekt: LV digital GmbH
    • Entwicklung eines Verfahrens zur Bildverbesserung von biomedizinischen Bilddaten wie Elektronen Miktoskopie ()
      Gefördertes Einzelprojekt: EMSIS GmbH
    • EXC 1003 PP-2017-10 The hatching box: Automated monitoring of the circadian clock during metamorphosis of the fly ()
      Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Exzellenzcluster | Förderkennzeichen: PP-2017-10
  • Publikationen

    • Kockwelp, Jacqueline; Thiele, Sebastian; Bartsch, Jannis; Haalck, Lars; Gromoll, Jörg; Schlatt, Stefan; Exeler, Rita; Bleckmann, Annalen; Lenz, Georg; Wolf, Sebastian; Steffen, Björn; Berdel, Wolfgang Eduard; Schliemann, Christoph; Risse, Benjamin; Angenendt, Linus. . ‘Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.’ Blood Advances 8, Nr. 1: 70–79. doi: 10.1182/bloodadvances.2023011076.
    • Junga, Anna; Kockwelp, Pascal; Valkov, Dimitar; Marschall, Bernhard; Hartwig, Sophia; Stummer, Walter; Risse, Benjamin; Holling, Markus.Virtual Reality based teaching – a paradigm shift in education?“ contributed to the 73. Jahrestagung Deutsche Gesellschaft für Neurochirurgie, Köln, . doi: 10.3205/22DGNC538.
    • Schulte L; Butz M; Becker M; Risse B; Schuck C. . ‘Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning .’ Journal of the Optical Society of America B . doi: 10.1364/JOSAB.506159. [accepted / in Press (not yet published)]
    • Beckmann, Daniel; Kockwelp, Jacqueline; Gromoll, Joerg; Kiefer, Friedemann;Risse, Benjamin. . ‘SAM meets Gaze: Passive Eye Tracking for Prompt-based Instance Segmentation.’ Proceedings of Machine Learning Research . [accepted / in Press (not yet published)]
    • Haalck, Lars; Risse, Benjamin. . ‘Solving the Plane-Sphere Ambiguity in Top-Down Structure-from-Motion.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Gebauer, Eike; Thiele, Sebastian; Ouvrard, Pierre; Sicard, Adrien; Risse, Benjamin. . ‘Towards a Dynamic Vision Sensor-based Insect Camera Trap.’ Contributed to the Winter Conference on Applications of Computer Vision 2024, Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Young S; Schiffer C; Wagner A; Patz J; Potapenko A; Herrmann L; Nordhoff V; Pock T; Krallmann C; Stallmeyer B; Röpke A; Kierzek M; Biagioni C; Wang T; Haalck L; Deuster D; Hansen JN; Wachten D; Risse B; Behre HM; Schlatt S; Kliesch S; Tüttelmann F; Brenker C; Strünker T. . ‘Human fertilization in vivo and in vitro requires the CatSper channel to initiate sperm hyperactivation.’ Journal of Clinical Investigation 134, Nr. 1. doi: 10.1172/JCI173564.
    • Winter NR; Blanke J; Leenings R; Ernsting J; Fisch L; Sarink K; Barkhau C; Emden D; Thiel K; Flinkenflügel K; Winter A; Goltermann J; Meinert S; Dohm K; Repple J; Gruber M; Leehr EJ; Opel N; Grotegerd D; Redlich R; Nitsch R; Bauer J; Heindel W; Gross J; Risse B; Andlauer TFM; Forstner AJ; Nöthen MM; Rietschel M; Hofmann SG; Pfarr JK; Teutenberg L; Usemann P; Thomas-Odenthal F; Wroblewski A; Brosch K; Stein F; Jansen A; Jamalabadi H; Alexander N; Straube B; Nenadic I; Kircher T; Dannlowski U; Hahn T. . ‘A Systematic Evaluation of Machine Learning--Based Biomarkers for Major Depressive Disorder.’ JAMA Psychiatry . doi: 10.1001/jamapsychiatry.2023.5083. [accepted / in Press (not yet published)]
    • Barrie, Robert; Haalck, Lars; Risse, Benjamin; Nowotny, Thomas; Graham, Paul; Buehlman, Cornelia. . ‘Trail using ants follow idiosyncratic routes in complex landscapes.’ Learning and Behavior s13420-023-00615. doi: https://doi.org/10.3758/s13420-023-00615-y.
    • Hätscher Ole , Junga Anna , Schulze Henriette , Kockwelp Pascal , Risse Benjamin , Back D. Mitja , Marschall Bernhard.Zusammenhang von Persönlichkeitsvariablen und Leistung in der virtuellen medizinischen Ausbildung.“ contributed to the Jahrestagung der Gesellschaft für medizinische Ausbildung (GMA) 2023, Osnabrück, . doi: 10.3205/23GMA273.
    • Mergen, M; Junga, Anna, Risse, Benjamin; Valkov, Dimitar; Graf, Norbert; Marschall, Bernhard; Medical Tr.AI.Ning Consortium. . „Immersive training of clinical decision making with AI driven virtual patients – a new VR platform called medical tr.AI.ning.“ GMS Journal for Medical Education 40, Nr. 2. doi: 10.3205/ZMA001600.
    • Butz, Marco; Abazi, Adrian S.; Ross, Rene; Risse, Benjamin; Schuck, Carsten. . ‘Inverse Design of Nanophotonic Devices using Dynamic Binarization.’ Optics Express 31, Nr. 10: 15747–15756. doi: 10.1364/OE.484484.
    • Wendland D; Becker M; Brückerhoff-Plückelmann F; Bente I; Busch K; Risse B; Pernice WH. . ‘Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder.’ Journal of the Optical Society of America B 40, Nr. 3: B35–B40.
    • Haalck L; Mangan M; Wystrach A; Clement L; Webb B; Risse B. . ‘CATER: Combined Animal Tracking & Environment Reconstruction.’ Science advances 9, Nr. 16: eadg2094.
    • Rösler W; Altenbuchinger M; Bae{ß}ler B; Beissbarth T; Beutel G; Bock R; Bubnoff N; Eckardt J; Foersch S; Loeffler CM; others. . ‘An overview and a roadmap for artificial intelligence in hematology and oncology.’ Journal of Cancer Research and Clinical Oncology 15: 1–10.
    • Butz M; Leifhelm A; Becker M; Risse B; Schuck C. . ‘A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning.’ In CLEO 2023, paper STh4G.3, edited by Optica Publishing Group, STh4G.3. San Jose: Optica Publishing Group. doi: 10.1364/CLEO_SI.2023.STh4G.3.
    • Butz, Marco; Leifhelm, Alexander; Becker, Marlon; Risse, Benjamin; Schuck, Carsten. . ‘A Novel Approach to Nanophotonic Black-Box Optimization Through Reinforcement Learning.’ In Q 30 Nano-optics, edited by DPG, 1. Hannover: DPG Springmeeting 2023.
    • Kockwelp, Jacqueline; Gromoll, Jörg; Wistuba, Joachim; Risse, Benjamin. . ‘EyeGuide - From Gaze Data to Instance Segmentation.’ Contributed to the The British Machine Vision Conference (BMVC), Aberdeen.
    • Becker, Marlon; Riegelmeyer, Jan; Seyfried, Maximilian; Ravoo, Bart-Jan; Schuck, Carsten; Risse, Benjamin. . ‘Adaptive Photo-Chemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems ‎ 5, Nr. 12: 2300229. doi: 10.1002/aisy.202300229 .
    • Haalck, Lars; Thiele, Sebastian; Risse, Benjamin. . ‘Tracking Tiny Insects in Cluttered Natural Environments using Refinable Recurrent Neural Networks.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Brückerhoff-Plückelmann, Frank; Bente, Ivonne; Becker, Marlon; Vollmar, Niklas; Farmakidis, Nikolaos; Lomonte, Emma; Lenzini, Francesco; Wright, C David; Bhaskaran, Harish; Salinga, Martin; Risse, Benjamin; Pernice, Wolfram HP. . ‘Event-driven adaptive optical neural network.’ Science advances 9, Nr. 42: eadi9127. doi: 10.1126/sciadv.adi9127.
    • Becker, Marlon; Drees, Dominik; Brückerhoff-Plückelmann, Frank; Schuck, Carsten; Pernice, Wolfram; Risse, Benjamin.Activation Functions in Non-Negative Neural Networks.“ contributed to the Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans, .
    • Pielage, Leon; Schmidle, Paul; Marschall, Bernhard; Risse, Benjamin.Diffusion Models in Dermatological Education: Flexible High Quality Image Generation for VR-based Clinical Simulations.“ contributed to the NeurIPS'23 Workshop: Generative AI for Education (GAIED), New Orleans, Louisiana, . [accepted / in Press (not yet published)]
    • Döll, Petra; Sester, Monika; Feuerhake, Udo; Frahm, Holger; Fritzsch, Bernadette; Hezel, Dominik C; Kaus, Boris; Kolditz, Olaf; Linxweiler, Jan; Müller Schmied, Hannes; Nyenah, Emmanuel; Risse, Benjamin; Schielein, Ulrich; Schlauch, Tobias; Streck, Thilo; van den Oord, Gijs. . Sustainable research software for high-quality computational research in the Earth System Sciences: Recommendations for universities, funders and the scientific community in Germany FIG GEO-LEO e-docs. doi: 10.23689/fidgeo-5805.
    • Bobe S, Beckmann D, Klump DM, Dierkes C, Kirschnick N, Redder E, Bauer N, Schäfers M, Erapaneedi R, Risse B, van de Pavert SA, Kiefer F. . ‘Volumetric imaging reveals VEGF-C-dependent formation of hepatic lymph vessels in mice.Frontiers in cell and developmental biology 10: 949896. doi: 10.3389/fcell.2022.949896.
    • Hahn T, Ernsting J, Winter NR, Holstein V, Leenings R, Beisemann M, Fisch L, Sarink K, Emden D, Opel N, Redlich R, Repple J, Grotegerd D, Meinert S, Hirsch JG, Niendorf T, Endemann B, Bamberg F, Kröncke T, Bülow R, Völzke H, von Stackelberg O, Sowade RF, Umutlu L, Schmidt B, Caspers S, Kugel H, Kircher T, Risse B, Gaser C, Cole JH, Dannlowski U, Berger K. . ‘An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling.’ Science advances 8, Nr. 1: eabg9471. doi: 10.1126/sciadv.abg9471.
    • Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T. . ‘Perspectives in machine learning for wildlife conservation.’ Nature Communications 13, Nr. 1: 792–807. doi: 10.1038/s41467-022-27980-y.
    • Kockwelp P, Junga A, Valkov D, Marschall B, Holling M, Risse B. . ‘Towards VR Simulation-Based Training in Brain Death Determination.’ In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), edited by IEEE, 287–292. 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW): Wiley-IEEE Press.
    • Thiele, Sebastian; Risse, Benjamin. . ‘Narrowing Attention in Capsule Networks.’ In 26th International Conference on Pattern Recognition, edited by IEEE, 2679–2685. 26th International Conference on Pattern Recognition (ICPR): Wiley-IEEE Press.
    • Kockwelp, Jacqueline; Thiele, Sebastian; Kockwelp, Pascal; Bartsch, Jannis; Schliemann, Christoph; Angenendt, Linus; Risse, Benjamin. . ‘Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia.’ In The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), edited by IEEE/CVF, 1825–1834. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: Wiley-IEEE Press.
    • Riegelmeyer, Jan; Eich, Alexander;Becker, Marlon;Risse, Benjamin;Schuck, Carsten.Development of a nanophotonic nonlinear unit for optical artificial neural networks.“ contributed to the DPG Springmeeting 2022, Erlangen, .
    • Butz, Marco; Leifhelm, Alexander; Becker, Marlon; Risse, Benjamin; Schuck, Carsten. . ‘Inverse Design of Nanophotonic Devices based on Reinforcement Learning.’ In Q 38 Photonics II, edited by DPG, 2. Erlangen: DPG Springmeeting 2022.
    • Bian A, Jiang X, Berh D, Risse B. . ‘Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations.’ IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, Nr. 3: 1184–1194.
    • Valkov Dimitar, Thiele Sebastian, Huesmann Karim, Risse Benjamin. . ‘Touch Recognition on Complex 3D Printed Surfaces using Filter Response Analysis.’ Contributed to the IEEE VR Workshop on Novel Input Devices and Interaction Techniques (NIDIT), Online. [online first]
    • Huesmann Karim, Rodriguez Luis Garcia, Linsen Lars, Risse Benjamin. . ‘The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks.’ In The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks.: Springer International Publishing.
    • Haalck Lars, Risse Benjamin. . ‘Embedded Dense Camera Trajectories in Multi-Video Image Mosaics by Geodesic Interpolation-based Reintegration.’ Contributed to the Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii.
    • Thiele Sebastian, Haalck Lars, Struffert Marvin, Scherber Christoph, Risse Benjamin. . ‘Towards Visual Insect Camera Traps.’ Contributed to the International Conference on Pattern Recognition (ICPR) Workshop on Visual observation and analysis of Vertebrate And Insect Behavior (VAIB), Milan.
    • Leenings R, Winter NR, Plagwitz L, Holstein V, Ernsting J, Sarink K, Fisch L, Steenweg J, Kleine-Vennekate L, Gebker J, Emden D, Grotegerd D, Opel N, Risse B, Jiang X, Dannlowski U, Hahn T . . ‘PHOTONAI-A Python API for rapid machine learning model development .’ PloS one 16. doi: 10.1371/journal.pone.0254062.
    • Huesmann K, Klemm S, Linsen L, Risse B. . Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization. arXiv e-print:2002.09237: CoRR.
    • Leenings R, Winter NR, Plagwitz L, Holstein V, Ernsting J, Steenweg J, Gebker J, Sarink K, Emden D, Grotegerd D, others. . ‘PHOTON--A Python API for Rapid Machine Learning Model Development.’ arXiv preprint arXiv:2002.05426 2020.
    • Neuert H, Deing P, Krukkert K, Naffin E, Steffes G, Risse B, Silies M, Klämbt C. . ‘The Drosophila NCAM homolog Fas2 signals independently of adhesion.’ Development 147, Nr. 2. doi: 10.1242/dev.181479.
    • Haalck L, Mangan M, Webb B, Risse B. . ‘Towards image-based animal tracking in natural environments using a freely moving camera.’ Journal of Neuroscience Methods 330: 108455. doi: 10.1016/j.jneumeth.2019.108455.
    • Gkanias E, Risse B, Mangan M, Webb B. . ‘From skylight input to behavioural output: a computational model of the insect polarised light compass.’ PLoS Computational Biology 15, Nr. 7: e1007123. doi: 10.1371/journal.pcbi.1007123.
    • Berh D, Scherzinger A, Otto N, Jiang X, Klämbt C, Risse B. . ‘Automatic non-invasive heartbeat quantification of Drosophila pupae.’ Computers in Biology and Medicine 93: 189–199.
    • Otto N, Marelja Z, Schoofs A, Kranenburg H, Bittern J, Yildirim K, Berh D, Bethke M, Thomas S, Rode S, Risse B, Jiang X, Pankratz M, Leimkuehler S, Klämbt C. . ‘The Sulfite Oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia.’ Nature Communications 9, Nr. 1: 3514.
    • Kiel M, Berh D, Daniel J, Otto N, Steege A, Jiang X, Liebau E, Risse B. . A Multi-Purpose Worm Tracker Based on FIM bioRxiv.
    • Risse B, Mangan M, Webb B.Possibilities, Constraints and Limitations of Image-based Animal Tracking in Natural Environments.“ contributed to the Measuring Behavior, Manchester, UK, . [online first]
    • Risse B, Mangan M, Stürzl W, Webb B. . ‘Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes.’ Environmental Modelling and Software 99: 88–100. doi: 10.1016/j.envsoft.2017.09.018.
    • Klemm S, Jiang X, Risse B. . ‘Deep distance transform to segment visually indistinguishable merged objects.’ Contributed to the Proc. of 40th German Conference on Pattern Recognition (GCPR), Stuttgart.
    • Almeida-Carvalho MJ, Berh D, ......, Jiang X, ......, Risse B, ......, Zlatic M. . ‘The Ol1mpiad: Concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae.’ Journal of Experimental Biology 220: 2452–2475. doi: 10.1242/jeb.156646.
    • Risse B, Berh D, Otto N, Klämbt C, Jiang X. . ‘FIMTrack: An open source tracking and locomotion analysis software for small animals.’ PLoS Computational Biology 13, Nr. 5: e1005530. doi: 10.1371/journal.pcbi.1005530.
    • Berh D, Risse B, Michels T, Otto N, Jiang X, Klämbt C. . ‘A FIM-based long-term in-vial monitoring system for Drosophila larvae.’ IEEE Transactions on Biomedical Engineering 64, Nr. 8: 1862–1874.
    • Risse B, Mangan M, Del Pero L, Webb B. . ‘Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera.’ International Conference on Computer Vision (ICCV), Workshop on Visual Wildlife Monitoring, Venice, Italy 2017: 2840–2849.
    • Otto N, Risse B, Berh D, Bittern J, Jiang X, Klämbt C. . ‘Interactions among Drosophila larvae before and during collision.’ Scientific Reports 11, Nr. 6: 31564. doi: 10.1038/srep31564.
    • Risse B, Mangan M, Webb B.Tracking, Mapping and Reconstruction. Modelling the Visual Perception of Desert Ants.“ contributed to the Animal Movement International Symposium, Lund, Sweden, .
    • Risse B, Mangan M, Webb B.Habitat3D: Recreating the History of Visual Experience of Individual Insects.“ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
    • Risse B, Mangan M, Del Pero L, Webb B.HabiTracks: Visual Tracking of Insects in Their Natural Habitat.“ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
    • Risse B, Otto N, Berh D, Jiang X, Kiel M, Klämbt C. . ‘FIM2c : A Multi-Colour, Multi-Purpose Imaging System to Manipulate and Analyse Animal Behaviour.IEEE Transactions on Biomedical Engineering 64: 1–1.
    • Risse B., Berh D., Otto N., Jiang X., Klämbt C. . ‘Quantifying subtle locomotion phenotypes of Drosophila larvae using internal structures based on FIM images.’ Computers in Biology and Medicine 63, Nr. null: 269–276. doi: 10.1016/j.compbiomed.2014.08.026.
    • Risse B, Berh D, Otto N, Jiang X, Klämbt C. . ‘Imaging Modalities for Semi-Translucent Animals and Their Impact on Quantitative Analysis.’ In VAIB Workshop, ICPR, 1––4.
    • B. Risse, N. Otto, D. Berh, X. Jiang, C. Klämbt. . ‘FIM imaging and FIMTrack: Two new tools allowing high-throughput and cost effective locomotion analysis.’ Journal of Visualized Experiments 94.
    • U. Lammel, M. Bechtold, B. Risse B, D. Berh, A. Fleige, I. Bunse, X. Jiang, C. Klämbt, S. Bogdan. . ‘The Drosophila FHOD1-like formin Knittrig acts through Rok to promote stress fiber formation and directed macrophage migration during the cellular immune response.Development 141, Nr. 6: 1366–1380. doi: 10.1242/dev.101352.
    • Tao J, Risse B, Jiang X. . ‘Stereo and Motion Based 3D High Density Object Tracking.’ Image and Video Technology 2014: 136–148. doi: 10.1007/978-3-642-53842-1_12.
    • Risse B, Berh D, Otto N, Jiang X, Klämbt C.FIM2C and the Analysis of Collision Behavior.“ contributed to the Flies, Worms and Robots: Combining Perspectives on Minibrains and Behavior Conference, Sant Feliu de Guixols, Spain, .
    • Jiang X, Dawood M, Gigengack F, Risse B, Schmid S, Tenbrinck D, Schäfers K. . ‘Biomedical Imaging: a Computer Vision Perspective.’ In Computer Analysis of Images and Patterns, edited by R. Wilson et al.: CAIP 2013, 1–19. Berlin, Heidelberg: Springer VDI Verlag.
    • Risse B, Jiang X, Klämbt C. . ‘FIM: Frustrated Total Internal Reflection Based Imaging for Biomedical Applications.’ ERCIM News 95, Nr. Image Understanding: 11–12.
    • M. Sander, A.J. Squarr, B. Risse, X. Jiang, S. Bogdan. . ‘Drosophila pupal macrophages - A versatile tool for combined ex vivo and in vivo imaging of actin dynamics at high resolution.European Journal of Cell Biology 2013. doi: 10.1016/j.ejcb.2013.09.003.
    • B. Risse, S. Thomas, N. Otto, T. Löpmeier, D. Valkov, X. Jiang, C. Klämbt. . ‘FIM, a novel FTIR-based imaging method for high throughput locomotion analysis.’ PloS one 8, Nr. 1: e53963. doi: 10.1371/journal.pone.0053963.
    • Risse B, Berh D, Tao J, Jiang X, Klette R, Klämbt C. . ‘Comparison of two 3D tracking paradigms for freely flying insects.’ EURASIP J Image Video Process 2013, Nr. 1: 57. doi: 10.1186/1687-5281-2013-57.
    • Risse B, Otto N, Berh D, Jiang X, Klämbt C.Tracking of Colliding Larvae.“ contributed to the Conference on Neurobiology of Drosophila, New York, USA, .
    • Schmidt I, Thomas S, Kain P, Risse B, Naffin E, Klämbt C. . ‘Kinesin heavy chain function in Drosophila glial cells controls neuronal activity.Journal of Neuroscience 32, Nr. 22: 7466–7476. doi: 10.1523/JNEUROSCI.0349-12.2012.
    • Tao J, Risse B, Jiang X, Klette R. . ‘3D Trajectory Estimation of Simulated Fruit Flies.’ In Proc 27th IVCNZ, Dunedin 2012.
    • Risse B, Thomas S, Jiang X, Klämbt C.FIM: FTIR Based Image Acquisition and Tracking.“ contributed to the Conference on Behavioral Neurogenetics of Drosophila Larva (Maggot Meeting), Ashburn, USA, .