title: Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
abstract: Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well.
authors.affiliation: Center for Mind and Brain, University of California-Davis, Davis, California, United States of America.
authors.forename: Jun Yi
authors.initials: JY
authors.lastname: Wang
authors.affiliation: Center for Mind and Brain, University of California-Davis, Davis, California, United States of America.
authors.forename: Michael M
authors.initials: MM
authors.lastname: Ngo
authors.affiliation: Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, University of California-Davis Medical Center, Sacramento, California, United States of America.|Department of Psychiatry and Behavioral Sciences, University of California-Davis, School of Medicine, Sacramento, California, United States of America.
authors.forename: David
authors.initials: D
authors.lastname: Hessl
authors.affiliation: Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, University of California-Davis Medical Center, Sacramento, California, United States of America.|Department of Pediatrics, University of California-Davis, School of Medicine, Sacramento, California, United States of America.
authors.forename: Randi J
authors.initials: RJ
authors.lastname: Hagerman
authors.affiliation: Center for Mind and Brain, University of California-Davis, Davis, California, United States of America.|Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, University of California-Davis Medical Center, Sacramento, California, United States of America.|Department of Psychology, University of California-Davis, Davis, California, United States of America.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Rivera
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001259:Ataxia / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology / Q000523:psychology; D001931:Brain Mapping / Q000379:methods / Q000592:standards; D001933:Brain Stem / Q000000981:diagnostic imaging* / Q000473:pathology; D002138:Calibration; D016022:Case-Control Studies; D002531:Cerebellum / Q000000981:diagnostic imaging* / Q000473:pathology; D005600:Fragile X Syndrome / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology / Q000523:psychology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods / Q000592:standards; D014202:Tremor / Q000000981:diagnostic imaging* / Q000473:pathology / Q000503:physiopathology / Q000523:psychology; D055815:Young Adult