A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment View Full Text


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Article Info

DATE

2013-09

AUTHORS

Mehdi Rahim, Marc-Emmanuel Bellemare, Rémy Bulot, Nicolas Pirró

ABSTRACT

In various imaging applications, shape variations are studied in order to define the transformations involved or to quantify a distance between each change performed. Regardless of the way the shapes may be extracted, with 2D imaging, shapes concern essentially curves or sets of points depending on the available data. Wether time is related to the shape variations or not, one can consider a set of shapes as the observation of the temporal evolution of an initial shape. In this context, we present a methodology aiming at quantifying the evolution of a set of contours without landmarks. Our characterization of temporal sequences is based on the large deformation diffeomorphic mapping paradigm and the shape representation based on currents, which allow both to propose a shape metric and a curve matching of the timed variations. Then, mechanics related features are extracted as they are physically meaningful and quite painless understandable. In this paper, the process is applied within the scope of a pelviperineology study. Available clinical diagnoses are combined with statistical analysis to show the soundness of the approach. Indeed, pelvic floor disorders are characterized by abnormal organ descents and deformations during abdominal strains. As they are soft-tissue organs, the pelvic organs have no fixed landmarks, in addition to wide shape differences. Routinely used, 2D sagittal mri sequences are segmented to provide the contour sets from which the characterization should highlight pelvic organ behaviors. We believe that a statistical analysis of these behaviors on several dynamic mri sequences could help to a better understanding of the pelvic floor pathophysiology. The methodology is applied on a dataset of 30 patients with different clinical diagnoses. Some promising results are presented, where the pathology detection capability of the deformation features is assessed, and the principal organ dynamics modes are computed, through an inter-patient analysis. Also, an organ parcellation is proposed thanks to the local deformation analysis, it identifies spatial references which are clinically relevant. More... »

PAGES

151-164

References to SciGraph publications

  • 2008-12. Large Deformation Diffeomorphic Metric Curve Mapping in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2009. Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 1999-06-25. Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study in INFORMATION PROCESSING IN MEDICAL IMAGING
  • 2010. Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2010
  • 2007. Using Statistical Shape Analysis for the Determination of Uterine Deformation States During Hydrometra in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2007
  • 1983-12. Eigenshape analysis of microfossils: A general morphometric procedure for describing changes in shape in MATHEMATICAL GEOSCIENCES
  • 2009-12. Registration of Anatomical Images Using Paths of Diffeomorphisms Parameterized with Stationary Vector Field Flows in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007-11. Multivariate Statistical Differences of MRI Samples of the Human Brain in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • 2009-09. Prediction model and prognostic index to estimate clinically relevant pelvic organ prolapse in a general female population in INTERNATIONAL UROGYNECOLOGY JOURNAL
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    http://scigraph.springernature.com/pub.10.1007/s10851-012-0391-6

    DOI

    http://dx.doi.org/10.1007/s10851-012-0391-6

    DIMENSIONS

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