Mohamed Mounir El Mendili1, Maria Petracca1, Kornelius Podranski1,2, Lazar Fleysher3, Sirio Cocozza1,4, Matilde Inglese1,3,5,6. 1. Department of Neurology, Icahn School of Medicine at Mount Sinai, NY. 2. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. 3. Department of Radiology, Icahn School of Medicine at Mount Sinai, NY. 4. Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy. 5. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, NY. 6. Department of Neurology, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, (DINOGMI) University of Genova, Genoa, Italy.
Abstract
BACKGROUND AND PURPOSE: The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. METHODS: We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). RESULTS: The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). CONCLUSIONS: SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.
BACKGROUND AND PURPOSE: The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. METHODS: We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). RESULTS: The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MSpatients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). CONCLUSIONS: SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.