title: Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation.
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±1.00mm (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.
authors.affiliation: School of Information Technology and Electrical Engineering, University of Queensland, Australia; The Australian E-Health Research Centre, CSIRO Digital Productivity, Australia. Electronic address: ales.neubert@uqconnect.edu.au.
authors.forename: Aleš
authors.initials: A
authors.lastname: Neubert
authors.affiliation: The Australian E-Health Research Centre, CSIRO Digital Productivity, Australia.
authors.forename: Jurgen
authors.initials: J
authors.lastname: Fripp
authors.affiliation: School of Human Movement Studies, University of Queensland, Australia.
authors.forename: Craig
authors.initials: C
authors.lastname: Engstrom
authors.affiliation: Department of Neuroradiology, University Hospital Heidelberg, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schwarz
authors.affiliation: Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Germany.
authors.forename: Marc-André
authors.initials: MA
authors.lastname: Weber
authors.affiliation: School of Information Technology and Electrical Engineering, University of Queensland, Australia.
authors.forename: Stuart
authors.initials: S
authors.lastname: Crozier
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007403:Intervertebral Disc / Q000473:pathology*; D007405:Intervertebral Disc Displacement / Q000175:diagnosis*; D008279:Magnetic Resonance Imaging; D012372:ROC Curve
keywords: Computer-aided diagnosis; Herniation; Intervertebral disc; Magnetic resonance imaging; Segmentation; Sparse optimization; Statistical shape model