Michael Rebsamen


PhD student

Biomedical Engineer, MSc.

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• Bachelor's degree in Computer Science, Bern University of Applied Sciences, Bern, CH

• Master's degree in Biomedical Engineering, University of Bern, Bern, CH


Research interests

• Neuroimaging derived biomarkers for neurodegenerative and neurological disorders

• Epilepsy

• Brain morphometry from structural MRI

• Deep learning and machine learning in medical image analysis

• Applications of AI in neuroradiology

• Translation of quantitative imaging biomarkers into clinical applications



• Predict and Monitor Epilepsy After a First Seizure: The Swiss-First Study


Prizes and awards

• Biomedical engineering prize 2019 for best MSc thesis in basic science for the project "Fast and accurate human brain morphometry estimation with deep learning"


Selected publications

Rebsamen M, Rummel C, Reyes M, Wiest R, McKinley R. "Direct cortical thickness estimation using deep learningbased anatomy segmentation and cortex parcellation", Human Brain Mapping, 2020. doi:10.1002/hbm.25159

Dobrocky T, Rebsamen M, Rummel C, Häni L, Mordasini P, Raabe A, Ulrich CT, Gralla J, Piechowiak EI, Beck J. "Monro-Kellie Hypothesis: Increase of Ventricular CSF Volume after Surgical Closure of a Spinal Dural Leak in Patients with Spontaneous Intracranial Hypotension", American Journal of Neuroradiology, 2020. doi:10.3174/ajnr.A6782

Rebsamen M, Suter Y, Wiest R, Reyes M, Rummel C. "Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning", Frontiers in Neurology, 2020. doi: 10.3389/fneur.2020.00244

Rebsamen M, Knecht U, Reyes M, Wiest R, Meier R, McKinley R. "Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation". Frontiers in Neuroscience, 2019. doi:10.3389/fnins.2019.01182

Full publication list



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