title: Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities.
abstract: Muscle volume measurements are essential for an array of diseases ranging from peripheral arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In the clinical setting, muscle volume is not routinely measured due to the lack of standardized ways for its repeatable quantification. In this paper, we present magnetic resonance muscle quantification (MRMQ), a method for the automatic quantification of thigh muscle volume in magnetic resonance imaging (MRI) scans. MRMQ integrates a thigh segmentation and nonuniform image gradient correction step, followed by feature extraction and classification. The classification step leverages prior probabilities, introducing prior knowledge to a maximum a posteriori classifier. MRMQ was validated on 344 slices taken from 60 MRI scans. Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively.
authors.affiliation: Division of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA. gbrunner@bcm.edu
authors.forename: Gerd
authors.initials: G
authors.lastname: Brunner
authors.forename: Vijay
authors.initials: V
authors.lastname: Nambi
authors.forename: Eric
authors.initials: E
authors.lastname: Yang
authors.forename: Anirudh
authors.initials: A
authors.lastname: Kumar
authors.forename: Salim S
authors.initials: SS
authors.lastname: Virani
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Kougias
authors.forename: Dipan
authors.initials: D
authors.lastname: Shah
authors.forename: Alan
authors.initials: A
authors.lastname: Lumsden
authors.forename: Christie M
authors.initials: CM
authors.lastname: Ballantyne
authors.forename: Joel D
authors.initials: JD
authors.lastname: Morrisett
mesh_terms: D000465:Algorithms; D001331:Automation; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007866:Leg / Q000473:pathology*; D008055:Lipids / Q000737:chemistry; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009132:Muscles / Q000473:pathology*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013848:Thigh / Q000473:pathology*
chemical_list: D008055:Lipids