Antal Horvath was born in Basel, Switzerland, in 1987. In 2007, he acquired his general qualification for university entrance at the Gymnasium Münchenstein, where he achieved an award for his matriculation project in programming a computer video game.
He then studied Mathematics with a focus on Numerical Analysis at the University of Basel. During his Bachelor and Master studies, he worked as a teaching assistant at the Mathematical Institute of Basel. He received his B.Sc. in Mathematics in 2011 and his M.Sc. in Mathematics in 2014. For his Doctoral studies, he joined the Medical Faculty of the University of Basel in 2015 to work on the research project Segmentation of the Gray and White Matter in the Human Spinal Cord ( read PhD dissertation), funded by the Swiss National Science Foundation.
Since January 2020, he is a Postdoctoral Researcher in the group of Prof. Dr. Philippe Cattin at the Department of Biomedical Engineering of the University of Basel, Switzerland, where he co-leads the Planning and Navigation Group.
E. Schnider, A. Horváth, G. Rauter, A. Zam, M. Müller-Gerbl,P.C. Cattin (2020) 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies. In: Liu M., Yan P., Lian C., Cao X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science, vol 12436. Springer, Cham. read
A. Horváth. Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images. 2019, Doctoral Thesis, University of Basel, Faculty of Medicine. read
C. Tsagkas, A. Horváth, A. Todea, J. Mueller, A. Altermatt, M. Leimbacher, S. Pezold, M. Weigel, T. Haas, M. Amann, L. Kappos, T. Sprenger, O.Bieri, P. Cattin, C. Granziera, K. Parmar. Automatic Quantification Pipeline for Spinal Cord Grey and White Matter in Multiple Sclerosis. In Proceedings of the 28th Annual Meeting of ISMRM, Sydney, Australia, April 2020.
C. Tsagkas, A. Horváth, A. Altermatt, S. Pezold, M. Weigel, T. Haas, M. Amann, L. Kappos, T. Sprenger, O. Bieri, P. Cattin, K. Parmar. Automatic Spinal Cord Gray Matter Quantification: A Novel Approach. American Journal of Neuroradiology, August 2019. read
C. Tsagkas, A. Horváth, M. Weigel, T. Haas, L. Kappos, T. Sprenger, O. Bieri, P. Cattin, and K. Parmar. Reliability Of Automatic Spinal Cord Gray Matter Segmentation Using Averaged Magnetization Inversion Recovery Acquisitions. In Proceedings of the 13th Annual ARSEP MRI Workshop, Paris, France, pages 28–29, 2018.
S. Andermatt, A. Horváth, S. Pezold, P. C. Cattin (2019) Pathology Segmentation Using Distributional Differences to Images of Healthy Origin. In: Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science, vol 11383. Springer, Cham. read
A. Horváth, C. Tsagkas, S. Andermatt, S. Pezold, K. Parmar, P. C. Cattin P. (2019) Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU. In: Zheng G., Belavy D., Cai Y., Li S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2018. Lecture Notes in Computer Science, vol 11397. Springer, Cham. read
A. Horváth, S. Pezold, M. Weigel, K. Parmar, P. C. Cattin (2017) High Order Slice Interpolation for Medical Images. In: Tsaftaris S., Gooya A., Frangi A., Prince J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2017. Lecture Notes in Computer Science, vol 10557. Springer, Cham. read
A. Horváth, C. Jud, S. Pezold, M. Weigel, C. Tsagkas, K. Parmar, O. Bieri, P. C. Cattin. A Principled Approach to Combining Inversion Recovery Images. In: Proceedings of the 26th Annual Meeting of ISMRM, Paris, France (June 2018)
A. Horváth, S. Pezold, M. Weigel, K. Parmar, O. Bieri, P. C. Cattin (2016) Variational Segmentation of the White and Gray Matter in the Spinal Cord Using a Shape Prior. In: Yao J., Vrtovec T., Zheng G., Frangi A., Glocker B., Li S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science, vol 10182. Springer, Cham. read
S. Pezold et al. (2016) Automatic, Robust, and Globally Optimal Segmentation of Tubular Structures. In: Ourselin S., Joskowicz L., Sabuncu M., Unal G., Wells W. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol 9902. Springer, Cham. read