Biography Dr. Simon Pezold
Dr. Simon Pezold
Center for medical Image Analysis & Navigation (CIAN)
Department of Biomedical Engineering
University of Basel
4123 Allschwil, Switzerland
Tel: +41 61 207 54 13
Simon Pezold received his Diplom degree (combined BSc/MSc equivalent) in Medical Informatics from the University of Heidelberg, Germany, in 2010. He conducted research for his thesis during a six-month visit at the University of Utah, Salt Lake City, USA, aiming at segmenting the carotid arteries in magnetic resonance images.
For his Ph.D. in Biomedical Engineering, Simon Pezold joined the group of Prof. Cattin at the medical faculty of the University of Basel. His Ph.D. project was concerned with quantifying atrophy in magnetic resonance images of the spinal cord for multiple sclerosis research. It was carried out in close collaboration with the University Hospital Basel and MIAC AG, Basel. He graduated in 2016 with magna cum laude.
Simon Pezold continued as a postdoctoral researcher at the Department of Biomedical Engineering, where he currently co-leads the Image Segmentation group at the Center for medical Image Analysis & Navigation (CIAN). His primary research interest lies in variational methods for image segmentation.
- Pezold, S., Horváth, A., Fundana, K., Tsagkas, C., Andělová, M., Weier, K., Amann, M., Cattin, P.C.: Automatic, Robust, and Globally Optimal Segmentation of Tubular Structures. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. pp. 362–370. Springer International Publishing (2016).
- Amann, M.*, Pezold, S.*, Naegelin, Y., Fundana, K., Andělová, M., Weier, K., Stippich, C., Kappos, L., Radue, E.-W., Cattin, P., Sprenger, T.: Reliable volumetry of the cervical spinal cord in MS patient follow-up data with cord image analyzer (Cordial). J Neurol. 263, 1364–1374 (2016).
- Pezold, S., Fundana, K., Amann, M., Andelova, M., Pfister, A., Sprenger, T., Cattin, P.: Automatic Segmentation of the Spinal Cord Using Continuous Max Flow with Cross-sectional Similarity Prior and Tubularity Features. In: Yao, J., Glocker, B., Klinder, T., and Li, S. (eds.) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. pp. 107–118. Springer International Publishing (2015).
- Pezold, S., Amann, M., Weier, K., Fundana, K., Radue, E., Sprenger, T., Cattin, P.: A Semi-automatic Method for the Quantification of Spinal Cord Atrophy. In: Yao, J., Klinder, T., and Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. pp. 143–155. Springer International Publishing (2014).
*equally contributing first authors