As I am not an active member of mtec staff I don't supervise theses anymore
External PhD Student
+49 (0)40 42878 3547
E.3087
Lennart Holstein (né Bargsten)
Curriculum Vitae
| 2018 - today | MTEC Institute / TUHH |
| 2017 - 2018 | Helmut Schmidt University |
| 2014 - 2017 | University of Hamburg (Physics, M.Sc.) |
| 2011 - 2014 | TUHH (Mechanical Engineering, B.Sc.) |
Interests
Deep learning for medical image analysis
- Focus on ultrasound image data
- Particularly interested in methods for performance improvement of deep learning models with limited data
- Generative adversarial networks (GANs) and simulations for data augmentation
Roles
- Research assistant
- PhD student
Publications
2022
- L. Maack, L. Holstein, A. Schlaefer (2022). GANs for generation of synthetic ultrasound images from small datasets. Current Directions in Biomedical Engineering. 8. (1), 17-20 [Abstract]
[doi] [www] [BibTex]
2021
-
L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer
(2021).
Attention via Scattering Transforms for Segmentation of Small Intravascular Ultrasound Data Sets.
In Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris (Eds.)
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning
PMLR:
34-47.
[Abstract] [www] [BibTex]
-
L. Bargsten, D. Klisch, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer
(2021).
Deep learning for guidewire detection in intravascular ultrasound images:.
Current Directions in Biomedical Engineering.
7
(1),
106-110.
[Abstract] [doi] [www] [BibTex]
-
L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, M. Grass, S. Blankenberg, M. Seiffert, A. Schlaefer
(2021).
Deep learning for calcium segmentation in intravascular ultrasound images:.
Current Directions in Biomedical Engineering.
7
(1),
96-100.
[Abstract] [doi] [www] [BibTex]
-
L. Bargsten, S. Raschka, A. Schlaefer
(2021).
Capsule networks for segmentation of small intravascular ultrasound image datasets.
International Journal of Computer Assisted Radiology and Surgery.
16
(8),
1243-1254.
[Abstract] [doi] [www] [BibTex]
-
L. Bargsten, K. A. Riedl, T. Wissel, F. J. Brunner, K. Schaefers, J. Sprenger, M. Grass, M. Seiffert, S. Blankenberg, A. Schlaefer
(2021).
Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks.
In Brett C. Byram and Nicole V. Ruiter (Eds.)
Medical Imaging 2021: Ultrasonic Imaging and Tomography
SPIE:
1-7.
[Abstract] [doi] [www] [BibTex]
2020
-
M. Seemann, L. Bargsten, A. Schlaefer
(2020).
Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation.
Current Directions in Biomedical Engineering.
6
(1),
20200015.
[Abstract] [doi] [www] [BibTex]
-
L. Bargsten, A. Schlaefer
(2020).
SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing.
International Journal of Computer Assisted Radiology and Surgery.
15
(9),
1427-1436.
[Abstract] [doi] [www] [BibTex]
2019
-
F. Sommer, L. Bargsten, A. Schlaefer
(2019).
IVUS-Simulation for Improving Segmentation Performance of Neural Networks via Data Augmentation.
CURAC 2019 Tagungsband Reutlingen
47-51.
[Abstract] [www] [BibTex]
-
L. Bargsten, M. Wendebourg, A. Schlaefer
(2019).
Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture.
In Proc. 2019 41st IEEE Engineering in Medicine and Biology Society (EMBC'19) Berlin, Germany
989-992.
[Abstract] [doi] [www] [BibTex]