Literature DB >> 27293519

TOPOLOGY PRESERVING AUTOMATIC SEGMENTATION OF THE SPINAL CORD IN MAGNETIC RESONANCE IMAGES.

Min Chen1, Aaron Carass2, Jennifer Cuzzocreo3, Pierre-Louis Bazin3, Daniel S Reich4, Jerry L Prince2.   

Abstract

Magnetic resonance images of the spinal cord play an important role in studying neurological diseases, particularly multiple sclerosis, where spinal cord atrophy can provide a measure of disease progression and disability. Current practices involve segmenting the spinal cord manually, which can be an inconsistent and time-consuming process. We present an automatic segmentation method for the spinal cord using a combination of deformable atlas based registration and topology preserving classification. Using real MR data, our method is shown to be highly accurate when compared to segmentations by manual raters. In addition, our results always maintain the correct topology of the spinal cord, therefore providing segmentations more consistent with the known anatomy.

Entities:  

Keywords:  Magnetic resonance imaging; Magnetization transfer images; Topology-preserving segmentation; digital homeomorphism; spinal cord segmentation

Year:  2011        PMID: 27293519      PMCID: PMC4902292          DOI: 10.1109/ISBI.2011.5872741

Source DB:  PubMed          Journal:  Proc IEEE Int Symp Biomed Imaging        ISSN: 1945-7928


  15 in total

1.  Adaptive fuzzy segmentation of magnetic resonance images.

Authors:  D L Pham; J L Prince
Journal:  IEEE Trans Med Imaging       Date:  1999-09       Impact factor: 10.048

2.  The adaptive bases algorithm for intensity-based nonrigid image registration.

Authors:  Gustavo K Rohde; Akram Aldroubi; Benoit M Dawant
Journal:  IEEE Trans Med Imaging       Date:  2003-11       Impact factor: 10.048

Review 3.  Measurement of spinal cord atrophy in multiple sclerosis.

Authors:  Xia Lin; Christopher R Tench; Nikos Evangelou; Timothy Jaspan; Cris S Constantinescu
Journal:  J Neuroimaging       Date:  2004-07       Impact factor: 2.486

4.  A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal.

Authors:  Stuart S C Burnett; George Starkschalla; Craig W Stevens; Zhongxing Liao
Journal:  Med Phys       Date:  2004-02       Impact factor: 4.071

5.  Spinal crawlers: deformable organisms for spinal cord segmentation and analysis.

Authors:  Chris McIntosh; Ghassan Hamarneh
Journal:  Med Image Comput Comput Assist Interv       Date:  2006

6.  Homeomorphic brain image segmentation with topological and statistical atlases.

Authors:  Pierre-Louis Bazin; Dzung L Pham
Journal:  Med Image Anal       Date:  2008-06-20       Impact factor: 8.545

7.  Multi-modal volume registration by maximization of mutual information.

Authors:  W M Wells; P Viola; H Atsumi; S Nakajima; R Kikinis
Journal:  Med Image Anal       Date:  1996-03       Impact factor: 8.545

8.  A knowledge-based approach to automatic detection of the spinal cord in CT images.

Authors:  Neculai Archip; Pierre-Jean Erard; Michael Egmont-Petersen; Jean-Marie Haefliger; Jean-Francois Germond
Journal:  IEEE Trans Med Imaging       Date:  2002-12       Impact factor: 10.048

9.  Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis.

Authors:  Mark A Horsfield; Stefania Sala; Mohit Neema; Martina Absinta; Anshika Bakshi; Maria Pia Sormani; Maria A Rocca; Rohit Bakshi; Massimo Filippi
Journal:  Neuroimage       Date:  2010-01-07       Impact factor: 6.556

10.  Quantification of spinal cord atrophy from magnetic resonance images via a B-spline active surface model.

Authors:  O Coulon; S J Hickman; G J Parker; G J Barker; D H Miller; S R Arridge
Journal:  Magn Reson Med       Date:  2002-06       Impact factor: 4.668

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  5 in total

1.  Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view.

Authors:  Min Chen; Aaron Carass; Jiwon Oh; Govind Nair; Dzung L Pham; Daniel S Reich; Jerry L Prince
Journal:  Neuroimage       Date:  2013-08-06       Impact factor: 6.556

Review 2.  On computerized methods for spine analysis in MRI: a systematic review.

Authors:  Marko Rak; Klaus D Tönnies
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-02-09       Impact factor: 2.924

3.  Multiparametric MRI correlates of sensorimotor function in the spinal cord in multiple sclerosis.

Authors:  Jiwon Oh; Kathleen Zackowski; Min Chen; Scott Newsome; Shiv Saidha; Seth A Smith; Marie Diener-West; Jerry Prince; Craig K Jones; Peter C M Van Zijl; Peter A Calabresi; Daniel S Reich
Journal:  Mult Scler       Date:  2012-08-13       Impact factor: 6.312

4.  Groupwise multi-atlas segmentation of the spinal cord's internal structure.

Authors:  Andrew J Asman; Frederick W Bryan; Seth A Smith; Daniel S Reich; Bennett A Landman
Journal:  Med Image Anal       Date:  2014-02-05       Impact factor: 8.545

5.  Shape Factor of the Spinal Cord: A Possible Predictor of Surgical Outcome for Intradural Extramedullary Spinal Tumors in the Thoracic Spine.

Authors:  Yoshihiro Matsumoto; Hirokazu Saiwai; Keiichiro Iida; Seiji Okada; Makoto Endo; Nokitaka Setsu; Toshifumi Fujiwara; Kenichi Kawaguchi; Yasuharu Nakashima
Journal:  Global Spine J       Date:  2021-01-07
  5 in total

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