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Abstract #1169

Automatic Quantification Pipeline for Spinal Cord Grey and White Matter in Multiple Sclerosis

Charidimos Tsagkas1,2,3, Antal Horvath4, Alexandra Todea5, Jannis Mueller1,2, Anna Altermatt2,3, Marina Leimbacher6, Simon Pezold4, Matthias Weigel2,4,7, Tanja Haas7, Michael Amann1,3,4, Ludwig Kappos1,2, Till Sprenger1,8, Philippe Cattin4, Cristina Granziera1,2,4, and Katrin Parmar1,2
1Neurologic Clinic and Policlinic, Departments of Medicine, Biomedical Engineering and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland, 2Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland, 3Medical Image Analysis Center (MIAC AG), Basel, Switzerland, 4Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland, 5Division of Diagnostic and Interventional Neuroradiology, Department of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland, 6Medical Faculty, University of Basel, Basel, Switzerland, 7Division of Radiological Physics, Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland, 8Department of Neurology, DKD Helios Klinik Wiesbaden, Wiesbaden, Germany

Currently, there is no gold-standard for spinal cord (SC) grey and white matter (GM/WM) quantification in multiple sclerosis (MS). In this work, the cervical SC of 24 MS patients and 24 healthy controls (HC) was scanned on a 3T MRI-system using averaged magnetization inversion recovery acquisitions. Manual segmentations were provided to train a “Multi-Dimensional Gated Recurrent Unit” neural network for subsequent automatic SC GM/WM/lesion segmentation. Accuracy of automatic segmentations was high and decreased in the order WM→GM→lesions and HC→MS. MS patients had reduced SC GM and WM compared to HC. Finally, SC GM, WM and lesions correlated with physical disability.

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