DeepMI Lab Members

martin.reuter (at) dzne.de
0000-0002-2665-9693
Scholar Citations
@deepmilab
deepmi

Martin is Assistant Professor of Radiology and of Neurology at Harvard Medical School and Massachusetts General Hospital (Assistant in Neuroscience, Dept. of Radiology and Dept. of Neurology). He is Director of AI in Medical Imaging (Deep-MI Lab) at the German Center for Neurodegenerative Diseases (DZNE), affiliated to the Martinos Center for Biomedical Imaging and the MIT Computer Science and Artificial Intelligence Lab.

Martin graduated in mathematics with a second major in computer science and a minor in business informatics in 2001 at Leibniz University, Hanover, Germany. He obtained his PhD (2005) in the area of computational and differential geometry from the department of electrical engineering and computer science with summa cum laude and was awarded a prize for his outstanding scientific accomplishments by the University of Hanover.

Supported by a Feodor-Lynen fellowship of the Alexander von Humboldt Foundation he moved to MIT (2006-08) and contributed novel methods for non-rigid shape analysis and processing, receiving a most cited paper award of the Computer-Aided Design journal for his ground-breaking manuscript on spectral shape analysis (ShapeDNA). Martin then moved to MGH/Harvard Medical School in 2008 and began his independent faculty career as Instructor at HMS in 2009/10 transitioning his focus to more applied medical image analysis topics. He contributed novel methods for unbiased longitudinal image processing of brain MRI and structural shape analysis for computer-aided diagnosis and prognosis. His methods are widely employed as part of the open source FreeSurfer software package to study neurodegeneration and assess disease modifying therapies, e.g., by the Alzheimer’s Disease Neuroimaging Initiative, the Rhineland Study, and other large cohort studies.

After receiving a competitive NIH Career Award (2014) for his research on computational methods for medical image analysis, Martin was appointed Assistant Professor by both the Radiology and the Neurology Departments at Harvard Medical School in 2015. He, further, accepted (2017) the offer to direct the AI in Medical Imaging Lab at the German Centerfor Neurodegenerative Diseases (DZNE) in Bonn, Germany. During his career so far, Martin has attracted approximately $3 mio worth of 3rd party funding.

david.kuegler (at) dzne.de
0000-0002-4101-819X
Scholar Citations
@kueglerd
dkuegler

David focuses of Learning aspects for Artificial Intelligence in Medicine. He has a strong interest in developing next generation learning strategies for medical imaging with an intrinsic inclusion of geometry and modeling. At DZNE he supports students and PhDs with his Deep Learning and medical image processing experience. As an interdisciplinary researcher, David graduated from RWTH Aachen with a degree in mechanical engineering focusing on learning for control engineering in medical robotics. Since then he worked at the TU Darmstadt as a PhD bringing learning to Computer-Assisted Interventions. In specific his PhD thesis addresses image-guided and electromagnetic tracking for temporal bone surgery.

Geometry Reconstruction, Interpretability

kersten.diers (at) dzne.de
kdiers

Kersten graduated from TU Dresden with a degree in Psychology and University of Heidelberg with a degree in Medical Biometry / Biostatistics. His work is at the intersection of applied methods development and empirical research, with a particular focus on shape analysis and statistical modeling of neuroimaging data.

Hippocampal Thickness Analysis, Shape Asymmetry, Harmonization, Quality Assessment, Bio-Statistics

santiago.estrada (at) dzne.de
0000-0003-0339-8870
Scholar Citations
santiestrada32

Santiago graduated from Technical University of Munich (TUM) with a degree in Biomedical Computing. His research focuses on the deployment of deep learning methods for quantifying imaging biomarkers in large cohort studies (e.g. Rhineland Study). When not at the lab, Santiago enjoys going to the movies and exploring the city.

Olfactory Bulb Segmentation, Localized Brain Age Prediction, Automated Tools for Retina Image Analysis

christian.ewert (at) dzne.de
christianewert

Christian obtained his Master degree in Computer Science at University of Bonn. He is interested in Deep Learning and its potential to reveal hidden patterns and relationships in neuroimaging data. He explores ways to apply Deep Learning to accelerated image reconstruction and fiber-tracking in diffusion-weighted MRI.

Diffusion Processing, Fiber Tracking

clemens.pollak (at) dzne.de
clepol

Clemens obtained his Master degree in Computer Science at Leibniz University Hanover. He is interested in Deep Learning, Data Science and Software Engineering. His research focusses on quantifying and controlling motion during MR acquisition using deep learning. Outside of the lab he enjoys windsurfing and hiking.

MR Motion Tracking

sebastian.rassmann (at) dzne.de
0000-0001-7119-9473
Scholar Citations
sRassmann

Sebastian has a multidisciplinary background, with Computer Science and Biomedicine degrees from Bonn University. He is interested in applying novel deep-learning methods to medical image analysis to aid patient care. He has previously worked on automating the analysis of microscopy and X-ray imagery. Currently, his research focuses on implementing diffusion models for MR contrast translation.

FLAIR synthesis

jingjing.wu (at) dzne.de

Jingjing earned her bachelor’s degree in clinical medicine and a master’s degree in neurology in China. She is a certified neurologist with over five years of clinical experience. She completed her PhD at DZNE in the Translational Biogerontology lab, where she studied the dynamics of aging in late life. Currently, her work focuses on continuous quality control of brain imaging.

Anatomical labeling, quality control