UNIVERSITY DEPARTMENT OF NEUROLOGY

Dünnwald

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
Vita

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

Publications

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. https://doi.org/10.1007/978-3-030-32695-1_12

 

Co-Authored

  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. https://doi.org/10.1007/978-3-658-25326-4_11

Last Modification: 08.09.2022 - Contact Person:

Sie können eine Nachricht versenden an: Webmaster
Sicherheitsabfrage:
Captcha
 
Lösung: