Daniel Stromer

Dr.-Ing. Daniel Stromer


Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 10.136
Martensstr. 3
91058 Erlangen

Work Experience

  • since 05/2019
    Collaboration Manager Digital Health, Siemens Healthineers AG
  • since 11/2016
    Research associate, Pattern Recognition Lab (LME), Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 09/2018 – 03/2019
    Exchange visiting student, Research Laboratory of Electronics (RLE), Massachusetts Insitute of Technology (MIT)
  • 08/2016 – 05/2019 (part-time)
    Software developer for a personnel management software, Zeit-H3 GmbH
  • 03/2016 – 10/2016 (part-time)
    Employee for digitization of outpatient reports, St. Anna Hospital Hoechstadt a.d. Aisch
  • 06/2013 – 09/2015
    Working student, B.Sc. and M.Sc. Thesis on predictive maintenance for X-ray systems and integration into the image chain, Siemens AG Sector Healthcare
  • 11/2011 – 05/2013
    Working student at Quality Installed Base developing test equipement for the image chain of X-ray systems, Siemens AG Sector Healthcare
  • 07/2006 – 09/2010
    X-ray system tester at AX Supply Chain Management – Make, Siemens AG Sector Healthcare

Education and Training

  • 11/2015 – 11/2019
    Dr.-Ing. Computer Science at the Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 10/2013 – 09/2015
    M.Sc. – Medical Engineering (focus on medical image and data processing), Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 10/2010 – 11/2013
    B.Sc. – Medical Engineering (focus on imaging systems), Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 09/2008 – 07/2010
    State Certified Technician – Information Technology, Fachschule für Techniker Erlangen
  • 09/2003 – 07/2006
    Training as Industrial Electrician (Device Technology, passed business units: MRI, CT, Audiology), Siemens AG Medical Solutions


Document Analysis

Within my research project “Opens external link in new windowDigitization of Fragile Historical Documents by Using 3-D X-ray Computed Tomography” I try to face the challenge of reading sealed documents. This can be books or scrolls that were damaged by external influences (e.g., fire, water) or they are too fragile to open them due to aging processes. As conventional digitization methods (using a scan robot) fail in these particular cases, non-invasive methods, such as X-ray CT or Terahertz imaging, can be capable of revealing the hidden contents. Trying to optimize the entire scan pipeline – from scanning to a final digitization – is my major goal.

Non-destructive Testing

Closely related to my PhD project, I am also highly interested in image processing and machine-vision algorithms for non-destructive testing of materials and goods. This includes 3-D reconstruction, image/volume processing and segmentation algorithms. I mainly work with X-ray CT, however, I am also interested in other technologies such as Terahertz or Ultrasound.

Medical Imaging

After 10 years of working in the field of healthcare engineering, I am still highly interested in the ongoing research. My Bachelor’s and Master’s theses were in collaboration with Siemens Healthcare where I tried to increase the quality of 3-D Scans and integrated a predictive maintenance system for C-arm CT’s. At the moment, I am an exchange visitor for seven months at the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT) working on segmentation/machine-vision algorithms for Optical Coherence Tomography (OCT).



Journal Articles

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Journal Articles

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Journal Articles

Conference Contributions


Journal Articles



Journal Articles

Conference Contributions


Conference Contributions


Conference Contributions


Conference Contributions


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