Literature DB >> 23107642

Intervertebral disc segmentation in MR images using anisotropic oriented flux.

Max W K Law1, Kengyeow Tay, Andrew Leung, Gregory J Garvin, Shuo Li.   

Abstract

This study proposes an unsupervised intervertebral disc segmentation system based on middle sagittal spine MR scans. The proposed system employs the novel anisotropic oriented flux detection scheme which helps distinguish the discs from the neighboring structures with similar intensity, recognize ambiguous disc boundaries, and handle the shape and intensity variation of the discs. Based on minimal user interaction, the proposed system begins with vertebral body tracking to infer the information regarding the positions and orientations of the target intervertebral discs. The information is employed in a set of image descriptors, which jointly constitute an energy functional describing the desired disc segmentation result. The energy functional is minimized by a level set based active contour model to perform disc segmentation. The proposed segmentation system is evaluated using a database consisting of 455 intervertebral discs extracted from 69 middle sagittal slices. It is demonstrated that the proposed method is capable of delivering accurate results for intervertebral disc segmentation.
Copyright © 2012 Elsevier B.V. All rights reserved.

Mesh:

Year:  2012        PMID: 23107642     DOI: 10.1016/j.media.2012.06.006

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


  8 in total

Review 1.  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

2.  Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Authors:  Zhongyi Han; Benzheng Wei; Stephanie Leung; Ilanit Ben Nachum; David Laidley; Shuo Li
Journal:  Neuroinformatics       Date:  2018-10

3.  Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images.

Authors:  Bilwaj Gaonkar; Yihao Xia; Diane S Villaroman; Allison Ko; Mark Attiah; Joel S Beckett; Luke Macyszyn
Journal:  IEEE J Transl Eng Health Med       Date:  2017-06-22       Impact factor: 3.316

4.  Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model.

Authors:  Dominik Gaweł; Paweł Główka; Tomasz Kotwicki; Michał Nowak
Journal:  Biomed Res Int       Date:  2018-04-29       Impact factor: 3.411

Review 5.  A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

Authors:  Ilona Karpiel; Adam Ziębiński; Marek Kluszczyński; Daniel Feige
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

6.  Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study.

Authors:  Nils Christian Lehnen; Robert Haase; Jennifer Faber; Theodor Rüber; Hartmut Vatter; Alexander Radbruch; Frederic Carsten Schmeel
Journal:  Diagnostics (Basel)       Date:  2021-05-19

7.  A method of localization and segmentation of intervertebral discs in spine MRI based on Gabor filter bank.

Authors:  Xinjian Zhu; Xuan He; Pin Wang; Qinghua He; Dandan Gao; Jiwei Cheng; Baoming Wu
Journal:  Biomed Eng Online       Date:  2016-03-22       Impact factor: 2.819

8.  Lumbar intervertebral disc characterization through quantitative MRI analysis: An automatic voxel-based relaxometry approach.

Authors:  Claudia Iriondo; Valentina Pedoia; Sharmila Majumdar
Journal:  Magn Reson Med       Date:  2020-02-14       Impact factor: 4.668

  8 in total

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