Semi-automatic segmentation of the tongue for 3D motion analysis with dynamic MRI

• Published in: 2013 IEEE 10th International Symposium on Biomedical Imaging Vol. 2013; pp. 1465 - 1468
• Main Authors: Junghoon Lee, Jonghye Woo, Fangxu Xing, Murano, Emi Z., Stone, Maureen, Prince, Jerry L.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 31.12.2013
• Subjects: deformable registration; Dynamics; Image reconstruction; Image resolution; Image segmentation; Magnetic resonance imaging; Motion segmentation; random walker; segmentation; super-resolution reconstruction; Tongue; random walker; segmentation; Tongue; deformable registration; super-resolution reconstruction
• ISBN: 1467364568; 9781467364560
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2013.6556811

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 semiautomatic segmentations of 52 volumes showed an average dice similarity coefficient (DSC) score of 0.9 with reduced segmented volume variability compared to manual segmentations.