Segmentation of the Gray and White Matter in the Human Spinal Cord


Neurological diseases like Multiple Sclerosis have an impact on the spinal cord. In this project, we study the effect of the disease on the different compartments of the spinal cord: gray matter and its surrounding white matter (GM/WM). To isolate these compartments, we segment magnetic resonance images of the spinal cord. Manual segmentation of such images is tedious and suffers from high intra- and interrater variability. We, therefore, aim at implementing deterministic and automatic segmentation algorithms to robustly and accurately mimic the manual segmentation process. We collaborate with the Division of Radiological Physics of the University Hospital Basel for developing image acquisition techniques that produce sharp images with high contrast between GM and WM, suitable for this segmentation task. Our developed segmentation algorithms we then use in collaboration with the Neurological Clinic and Polyclinic of the University Hospital Basel to calculate longitudinal atrophy rates of Multiple Sclerosis patients.

Project leader: Antal Horvath

Dr. Antal Horváth

Dr. Antal Horváth antal.horvath@unibas.ch

Dr. Charidimos Tsagkas

Dr. Charidimos Tsagkas charidimos.tsagkas@unibas.ch

Philippe Cattin

Prof. Dr. Philippe Cattin philippe.cattin@unibas.ch

Prof. Dr. Oliver Bieri

Prof. Dr. Oliver Bieri oliver.bieri@unibas.ch

PD Dr. med. Katrin Parmar

PD Dr. med. Katrin Parmar katrin.parmar@unibas.ch

Dr. Matthias Weigel

Dr. Matthias Weigel matthias.weigel@unibas.ch

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.

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