Artificial Intelligence in Neuroradiology

Principle Investigators

Univ.-Prof. Dr. Marc Schlamann, MD
Publications (PubMed)

Dr. Kai Roman Laukamp, MD
Publications (PubMed)

Dr. Lenhard Pennig, MD
Publications (PubMed)

Senior Advisors

Univ.-Prof. Dr. Jan Borggrefe, MD (Johannes Wesling Klinikum Minden - University Hospitals of the Ruhr-University Bochum)

Michael Perkuhn, MD (Philips GmbH Innovative Technologies)

Research Topics

Neurooncology

Our AI research group aims to develop novel methods to improve brain imaging and establish new approaches for non-invasive tumor characterization. Recent advances in processing of imaging data with use of Artificial Intelligence and introduction of quantitative MR imaging sequences provide numerous opportunities for clinicians to improve patient management and therapy planning.

One of our main interests is to automatize brain tumor detection and consecutive segmentation using Deep Learning based techniques to free up radiologists’ resources and enhance reproducibility of imaging data. In combination with newly developed imaging sequences such as MR Fingerprinting, we want to determine tumor biology, non-invasively. Finally, we use Artificial Intelligence for studies regarding non-invasive multiparametric diagnostics and studies evaluating multimodal tumor therapy.

Neurovascular Imaging

Another focus of our research group is the automated detection of neurovascular pathologies with special interest in intracranial aneurysms. Further, we want to use automatically generated segmentations to stratify patient risk and facilitate endovascular or surgical treatment.

Grant support

  • Faculty of Medicine and University Hospital Cologne

Collaborations

  • Institute for Diagnostic Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling Klinikum Minden, University Hospitals of the Ruhr-University Bochum, Germany
  • Department of Neurosurgery and Stereotaxy, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  • Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany
  • Philips GmbH Innovative Technologies, Aachen, Germany
Selected Publications

  • Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol. 2019
  • Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning. J Magn Reson Imaging. 2020
  • Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine. Invest Radiol. 2018
  • Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading. World Neurosurg. 2019
  • Automated Meningioma Segmentation in Multiparametric MRI: Comparable Effectiveness of a Deep Learning Model and Manual Segmentation. Clin Neuroradiol. 2020

Group members

Dr. Liliana Caldeira, PhD
Dr. Lukas Goertz, MD
Dr. Cornelia Hoyer, MD
Dr. Stephanie Jünger, MD
PD Dr. Christoph Kabbasch, MD
Mr. Jonathan Kottlors, MD
Dr. Simon Lennartz, MD
Mr. Michael Perkuhn, MD
Dr. Rahil Shahzad, PhD
Dr. Marco Timmer, MD
Mr. Frank Thiele, MSc
Dr. Niklas von Spreckelsen, MD
Dr. David Zopfs, MD

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