Literature DB >> 25487963

Segmentation of tongue muscles from super-resolution magnetic resonance images.

Bulat Ibragimov1, Jerry L Prince2, Emi Z Murano3, Jonghye Woo4, Maureen Stone5, Boštjan Likar6, Franjo Pernuš6, Tomaž Vrtovec6.   

Abstract

Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Atlasing; Game theory; Human tongue; Magnetic resonance imaging; Segmentation

Mesh:

Year:  2014        PMID: 25487963      PMCID: PMC4294977          DOI: 10.1016/j.media.2014.11.006

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  19 in total

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Authors:  Bulat Ibragimov; Boštjan Likar; Franjo Pernus; Tomaz Vrtovec
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3.  Factors influencing postoperative speech function of tongue cancer patients following reconstruction with fasciocutaneous/myocutaneous flaps--a multicenter study.

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Journal:  Int J Oral Maxillofac Surg       Date:  2007-03-23       Impact factor: 2.789

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Authors:  Lisa Tang; Tim Bressmann; Ghassan Hamarneh
Journal:  Med Image Anal       Date:  2012-08-01       Impact factor: 8.545

7.  Shape representation for efficient landmark-based segmentation in 3-d.

Authors:  Bulat Ibragimov; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec
Journal:  IEEE Trans Med Imaging       Date:  2014-04       Impact factor: 10.048

8.  SEMI-AUTOMATIC SEGMENTATION OF THE TONGUE FOR 3D MOTION ANALYSIS WITH DYNAMIC MRI.

Authors:  Junghoon Lee; Jonghye Woo; Fangxu Xing; Emi Z Murano; Maureen Stone; Jerry L Prince
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2013-12-31

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Journal:  CA Cancer J Clin       Date:  2005 Mar-Apr       Impact factor: 508.702

10.  Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization.

Authors:  René Donner; Bjoern H Menze; Horst Bischof; Georg Langs
Journal:  Med Image Anal       Date:  2013-03-17       Impact factor: 8.545

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3.  A Spatio-Temporal Atlas and Statistical Model of the Tongue During Speech from Cine-MRI.

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Journal:  IEEE J Biomed Health Inform       Date:  2022-03-07       Impact factor: 7.021

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