Dünnwald
MSc Max Dünnwald
Medicine and Digitalization - MedDigit
Automated Image Segmentation and Feature Extraction; Radiomics
Work experience
Since 10/2018
Research associate (research focus: robust, fully-automated image segmentation and extraction of biomarkers, Radiomics), Workgroup Medicine and Digitalization – MedDigit, Dept. of Neurology, Otto-von-Guericke-Universität, Magdeburg, Germany
10/2017-9/2018
Research assistant, Leibniz-Institute for Neurobiology - LIN, Magdeburg
Research field: further development of the software, which was created in the context of the Bachelor thesis
Educational background
4/2017-10/2018
Computer Science (Master of Science), Otto-von-Guericke-Universität, Magdeburg, Germany
Master thesis: “Evaluation of Deep Learning Methods for the Automatic Segmentation of Vertebral Metastases in MR Images” (Note: 1,0)
10/2013-4/2017
Computational visualistics (Bachelor of Science), Otto-von-Guericke-Universität, Magdeburg, Germany
Bachelor thesis: “Automatic detection and measurement of signals in Calcium-Imaging-acquisitions” (Note: 1,0)
7/2013
High school graduation, Europagymnasium “Richard von Weizsäcker”, Thale, Germany
Peer-Reviewed Conference Papers
First and Senior Authored
- Dünnwald M, Betts MJ, Sciarra A, Düzel E, Oeltze-Jafra S. Automated Segmentation of the Locus Coeruleus from Neuromelanin-sensitive 3T MRI using Deep Convolutional Networks. BVM 2020, Accepted manuscript
- Dubost F, Dünnwald M, Huff D, Scheumann V, Schreiber F, Vernooij M, Niessen W, Skalej M, Schreiber S, Oeltze-Jafra S, de Bruijne M. Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites. Workshop MLCN 2019. In: Zhou L. et al. (eds) OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 2019, MLCN 2019. Lecture Notes in Computer Science, vol 11796. Springer, Cham. https://doi.org/10.1007/978-3-030-32695-1_12
Co-Authored
- Sciarra A, Chatterjee S, Dünnwald M, Speck O, Oeltze-Jafra S. Evaluation of Deep Learning Techniques for Motion Artifacts Removal. ISMRM 2020, Sydney, Australia. Accepted Manuscript
- Sciarra A, Dünnwald M, Mattern H, Speck O, Oeltze-Jafra S. Super-Resolution with Conditional-GAN for MR Brain Images. ISMRM 2020, Sydney, Australia. Accepted Manuscript
- Hille G, Dünnwald M, Becker M, Steffen J, Saalfeld S, Tönnies K. (2019) Segmentation of Vertebral Metastases in MRI Using an U-Net like Convolutional Neural Network. In: Handels H, Deserno T, Maier A, Maier-Hein K, Palm C, Tolxdorff T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_11