Automatic Spinal Cord Gray Matter Quantification: A Novel Approach Article Swipe

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reproducibility medicine spinal cord segmentation nuclear medicine standard deviation similarity (geometry) fully automatic cord coefficient of variation sørensen–dice coefficient pattern recognition (psychology) artificial intelligence image segmentation computer science mathematics statistics surgery psychiatry mechanical engineering engineering image (mathematics)

Charidimos Tsagkas , Antal Horváth , Anna Altermatt , Simon Pezold , Matthias Weigel , Tanja Haas , Michael Amann , Ludwig Kappos , Till Sprenger , Oliver Bieri , Philippe C. Cattin , Katrin Parmar ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.3174/ajnr.a6157 · OA: W2969292425

Our novel approach including the averaged magnetization inversion recovery acquisitions sequence and a fully-automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation. This pipeline is promising for both the exploration of longitudinal structural GM changes and application in clinical settings in disorders affecting the spinal cord.