Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation

Neubert, Ales, Jurgen, Fripp, Engstrom, Craig M., Schwarz, Daniel, Weber, Marc-André and Crozier, Stuart (2015) Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation. Computerized Medical Imaging and Graphics, 46 11-19. doi:10.1016/j.compmedimag.2015.05.002

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Author Neubert, Ales
Jurgen, Fripp
Engstrom, Craig M.
Schwarz, Daniel
Weber, Marc-André
Crozier, Stuart
Title Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation
Journal name Computerized Medical Imaging and Graphics   Check publisher's open access policy
ISSN 0895-6111
1879-0771
Publication date 2015
Sub-type Article (original research)
DOI 10.1016/j.compmedimag.2015.05.002
Open Access Status File (Author Post-print)
Volume 46
Start page 11
End page 19
Total pages 9
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Collection year 2016
Language eng
Abstract Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07 ± 1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation.
Keyword Statistical shape model
Sparse optimization
Computer-aided diagnosis
Segmentation
Intervertebral disc
Herniation
Magnetic resonance imaging
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 28 May 2015, 11:46:43 EST by Craig Engstrom on behalf of School of Human Movement and Nutrition Sciences