M. Sc. Niclas Erben
Ratzeburger Allee 160
23562 Lübeck
Gebäude 64,
Raum 83
| Email: | niclas.erben(at)uni-luebeck.de |
| Phone: | +49 451 31015227 |
| Fax: | +49 451 31015204 |
Short Biography
Niclas Erben, né Bockelmann received his M.Sc. in Medical Engineering Science from the University of Lübeck in early 2020. He joined the Institute of Robotics and Cognitive Systems at the University of Lübeck as a PhD student and research assistant in April 2020. In his work he focuses on the application of signal processing and machine learning of biomedical signals in computer-assisted surgery.
Research Interests
- Artificial intelligence and machine learning
- Biomedical signal processing
- 2D/3D medical image processing
Memberships
German Society for Biomedical Engineering (DGBMT)
2022
Niclas
Bockelmann,
Daniel
Schetelig,
M.
Bonsanto,
Steffen
Buschschlüter, and
Floris
Ernst,
Intelligent ultrasonic-aspirator for CNS/ tumor tissue differentiation -- a feasibility study using machine learning, Köln , 2022.
Intelligent ultrasonic-aspirator for CNS/ tumor tissue differentiation -- a feasibility study using machine learning, Köln , 2022.
| DOI: | 10.3205/22dgnc188 |
| File: | 22dgnc188 |
Niclas
Bockelmann,
Daniel
Schetelig,
Denise
Kesslau,
Steffen
Buschschlüter,
Floris
Ernst, and
M.
Bonsanto,
Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation, International Journal of Computer Assisted Radiology and Surgery , 2022.
Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation, International Journal of Computer Assisted Radiology and Surgery , 2022.
| DOI: | 10.1007/s11548-022-02713-0 |
| File: | s11548-022-02713-0 |
2021
Ana
Estrada Lugo,
Felix
Haxthausen,
Niclas
Bockelmann, and
Floris
Ernst,
Automatic Segmentation of the Femoral Artery from 2D Ultrasound Images, Infinite Science Publishing GmbH, 2021.
Automatic Segmentation of the Femoral Artery from 2D Ultrasound Images, Infinite Science Publishing GmbH, 2021.
| ISBN: | 9783945954652 |
Ana
Estrada Lugo,
Niclas
Bockelmann, and
Felix
Haxthausen,
Sequential U-Net Architecture for Automatic Femoral Artery Segmentation in Ultrasound Images, Current Directions in Biomedical Engineering , vol. 7, no. 1, pp. 158-161, 2021.
Sequential U-Net Architecture for Automatic Femoral Artery Segmentation in Ultrasound Images, Current Directions in Biomedical Engineering , vol. 7, no. 1, pp. 158-161, 2021.
| DOI: | 10.1515/cdbme-2021-1034 |
| File: | cdbme-2021-1034 |
Niclas
Bockelmann,
Denise
Kesslau,
M.
Bonsanto,
Steffen
Buschschlüter, and
Floris
Ernst,
Towards machine learning-based tissue differentiation using an ultrasonic aspirator: computer assisted radiology and surgery proceedings of the 35th international Congress and exhibition Munich, Germany, June 21--25, 2021, 2021. pp. 107-108.
Towards machine learning-based tissue differentiation using an ultrasonic aspirator: computer assisted radiology and surgery proceedings of the 35th international Congress and exhibition Munich, Germany, June 21--25, 2021, 2021. pp. 107-108.
| DOI: | 10.1007/s11548-021-02375-4 |
| File: | s11548-021-02375-4 |
2019
Niclas
Bockelmann,
Jan
Graßhoff,
Lasse
Hansen,
Giacomo
Bellani,
Mattias
Heinrich, and
Philipp
Rostalski,
Deep Learning for Prediction of Diaphragm Activity from the Surface Electromyogram, Current Directions in Biomedical Engineering , vol. 5, no. 1, pp. 17-20, 2019. De Gruyter.
Deep Learning for Prediction of Diaphragm Activity from the Surface Electromyogram, Current Directions in Biomedical Engineering , vol. 5, no. 1, pp. 17-20, 2019. De Gruyter.
Niclas
Bockelmann,
Diana
Krüger,
D.C. Florian
Wieland,
Berit
Zeller-Plumhoff,
Niccoló
Peruzzi,
Silvia
Galli,
Regine
Willumeit-Römer,
Fabian
Wilde,
Felix
Beckmann,
Jörg
Hammel,
Julian
Moosmann, and
Mattias
Heinrich,
Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms, arXiv preprint arXiv:1908.04173 , 2019.
Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms, arXiv preprint arXiv:1908.04173 , 2019.
