MSc Max Dünnwald

Medical Faculty / Dept of Neurology
Medicine and Digitalization - MedDigit
Automated Image Segmentation and Feature Extraction; Radiomics
Leipziger Straße, 44, 39120, Magdeburg, Haus 66, Raum E229   vCard

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



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


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)


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)


High school graduation, Europagymnasium Richard von Weizsäcker, Thale, Germany


Peer-Reviewed Conference Papers 


First and Senior Authored 

  1. 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
  2. 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.



  1. 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
  2. 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
  3. 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.

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