Abstract #0886
Brain tissue segmentation on 3D-FLAIR weighted images in multiple sclerosis
Samantha Noteboom1, Martijn D. Steenwijk1, David R. van der Nederpelt2, Eva M.M. Strijbis3, Bastiaan Moraal2, Frederik Barkhof2,4, Jeroen J.G. Geurts1, Matthan W. A. Caan5, Hugo Vrenken2, and Menno M Schoonheim1
1Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 2Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 3Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 4Neurology and Healthcare Engineering, UCL London, Amsterdam, United Kingdom, 5Biomedical Engineering & Physics, Amsterdam UMC, location AMC, Amsterdam, Netherlands
Synopsis
Conventional brain segmentation approaches typically require high resolution 3D-T1 weighted images, which are often unavailable in clinical multiple sclerosis (MS) protocols. Recently, SynthSeg was released which allows 3D-FLAIR to be used for segmentations. This study compared segmentation on 3D-FLAIR with SynthSeg to segmentation in the same patients on 3D-T1 (SynthSeg, FastSurfer and SAMSEG). Brain segmentation was performed on 100 patients with 3D-T1 and 3D-FLAIR images from research and clinical datasets. ICC results showed good comparability for brain tissue, ventricle and grey matter assessments in research data. In clinical data only good comparability was found for ventricle segmentation.
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