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 Barkhof^2,4, 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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