title: Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI.
abstract: Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.
authors.affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA. Electronic address: junghoon@jhu.edu.
authors.forename: Junghoon
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Neural and Pain Sciences, University of Maryland Dental School, Baltimore, MD, USA; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Jonghye
authors.initials: J
authors.lastname: Woo
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Fangxu
authors.initials: F
authors.lastname: Xing
authors.affiliation: Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Emi Z
authors.initials: EZ
authors.lastname: Murano
authors.affiliation: Department of Neural and Pain Sciences, University of Maryland Dental School, Baltimore, MD, USA; Department of Orthodontics, University of Maryland Dental School, Baltimore, MD, USA.
authors.forename: Maureen
authors.initials: M
authors.lastname: Stone
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013060:Speech / Q000502:physiology*; D013382:Subtraction Technique; D014059:Tongue / Q000033:anatomy & histology* / Q000502:physiology*; D014584:User-Computer Interface
keywords: Deformable registration; Dynamic MRI; Motion; Random walker; Segmentation; Super-resolution reconstruction; Tongue