SEMI-AUTOMATIC SEGMENTATION OF THE TONGUE FOR 3D MOTION ANALYSIS WITH DYNAMIC MRI.
Accurate segmentation is an important preprocessing step for measuring the internal deformation of the tongue during speech and swallowing using 3D dynamic MRI. In an MRI stack, manual segmentation of every 2D slice and time frame is time-consuming due to the large number of volumes captured over the entire task cycle. In this paper, we propose a semi-automatic segmentation workflow for processing 3D dynamic MRI of the tongue. The steps comprise seeding a few slices, seed propagation by deformable registration, random walker segmentation of the temporal stack of images and 3D super-resolution volumes. This method was validated on the tongue of two 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 52 volumes showed an average dice similarity coefficient (DSC) score of 0.9 with reduced segmented volume variability compared to manual segmentations.
Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD, USA ; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Junghoon
J
Lee
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA ; Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA.
Jonghye
J
Woo
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Fangxu
F
Xing
Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.
Emi Z
EZ
Murano
Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA.
Maureen
M
Stone
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Jerry L
JL
Prince
Tongue; deformable registration; random walker; segmentation; super-resolution reconstruction