M. Sc. Daniel Wulff
Ratzeburger Allee 160
23562 Lübeck
Gebäude 64,
Raum 87
| Email: | d.wulff(at)uni-luebeck.de |
| Phone: | +49 451 31015225 |
| Fax: | +49 451 31015204 |
Kurzbiographie
Daniel Wulff schloss 2019 das Studium der Medizinischen Ingenieurwissenschaft an der Universität zu Lübeck mit dem M.Sc. ab. Während seines Studiums arbeitete er in einem Medizintechnik Unternehmen an der Detektion von Patientenbewegungen in der Cone-Beam CT Bildgebung. Seit Mitte 2019 ist Daniel Wulff als Doktorand am Institut für Robotik und Kognitive Systeme angestellt und beschäftigt sich mit Deep Learning basiertem Target Tracking in 4D Ultraschall für die Strahlentherapie.
Forschungsinteressen
- Echtzeit-Bewegungskompensation in der Strahlentherapie (Tracking)
- Künstliche Intelligenz und Maschinelles Lernen
- Medizinische Bildverarbeitung
Mitgliedschaften
- VDE (Verband der Elektrotechnik Elektronik Informationstechnik e.V.)
- DGBMT (Deutsche Gesellschaft für Biomedizinische Technik)
- GI (Gesellschaft für Informatik e.V.)
2023
Daniel
Wulff,
Jannis
Hagenah, and
Floris
Ernst,
Landmark tracking in 4D ultrasound using generalized representation learning, Int J CARS , vol. 18, pp. 493-500, 2023.
Landmark tracking in 4D ultrasound using generalized representation learning, Int J CARS , vol. 18, pp. 493-500, 2023.
Daniel
Wulff,
Timon
Dohnke,
Ngoc Thinh
Nguyen, and
Floris
Ernst,
Towards Realistic 3D Ultrasound Synthesis: Deformable Augmentation using Conditional Variational Autoencoders, IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) , pp. 821-826, 2023.
Towards Realistic 3D Ultrasound Synthesis: Deformable Augmentation using Conditional Variational Autoencoders, IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) , pp. 821-826, 2023.
| DOI: | 10.1109/CBMS58004.2023.00326 |
| Datei: | CBMS58004.2023.00326 |
2022
Jonas
Osburg,
Daniel
Wulff, and
Floris
Ernst,
Generalized Automatic Probe Alignment based on 3D Ultrasound, 2022. pp. 58-61.
Generalized Automatic Probe Alignment based on 3D Ultrasound, 2022. pp. 58-61.
| DOI: | 10.1515/cdbme-2022-0015 |
| Datei: | cdbme-2022-0015 |
2021
Daniel
Wulff,
Jannis
Hagenah, and
Floris
Ernst,
Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound, 2021.
Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound, 2021.
| Datei: | 2201.03319 |
Daniel
Wulff,
Mohamad
Mehdi,
Floris
Ernst, and
Jannis
Hagenah,
Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study, 2021. pp. 755-758.
Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study, 2021. pp. 755-758.
| DOI: | 10.1515/cdbme-2021-2193 |
| Datei: | cdbme-2021-2193 |
Felix
Haxthausen,
Sven
Böttger,
Daniel
Wulff,
Jannis
Hagenah,
Verónica
García-Vázquez, and
Svenja
Ipsen,
Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends, Current Robotics Reports , vol. 2, pp. 55-71, 2021.
Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends, Current Robotics Reports , vol. 2, pp. 55-71, 2021.
Svenja
Ipsen,
Daniel
Wulff,
Ivo
Kuhlemann,
Achim
Schweikard, and
Floris
Ernst,
Towards automated ultrasound imaging -- robotic image acquisition in liver and prostate for long-term motion monitoring, Physics in Medicine Biology , vol. 66, no. 9, pp. 094002, 2021.
Towards automated ultrasound imaging -- robotic image acquisition in liver and prostate for long-term motion monitoring, Physics in Medicine Biology , vol. 66, no. 9, pp. 094002, 2021.
| DOI: | 10.1088/1361-6560/abf277 |
| Datei: | abf277 |
2020
Daniel
Wulff,
Jannis
Hagenah,
Svenja
Ipsen, and
Floris
Ernst,
Learning Local Feature Descriptions in 3D Ultrasound, 2020. pp. 323-330.
Learning Local Feature Descriptions in 3D Ultrasound, 2020. pp. 323-330.
| DOI: | 10.1109/BIBE50027.2020.00059 |
| Datei: | BIBE50027.2020.00059 |
2019
Daniel
Wulff,
Ivo
Kuhlemann,
Floris
Ernst,
Achim
Schweikard, and
Svenja
Ipsen,
Robust motion tracking of deformable targets in the liver using binary feature libraries in 4D ultrasound, 2019. pp. 601-604.
Robust motion tracking of deformable targets in the liver using binary feature libraries in 4D ultrasound, 2019. pp. 601-604.
| DOI: | 10.1515/cdbme-2019-0151 |
| Datei: | cdbme-2019-0151 |
