Variational problems on flows of diffeomorphisms for image matching
Quarterly of Applied Mathematics
Statistical Shape Analysis Using Fixed Topology Skeletons: Corpus Callosum Study
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Multivariate Statistical Differences of MRI Samples of the Human Brain
Journal of Mathematical Imaging and Vision
Large Deformation Diffeomorphic Metric Curve Mapping
International Journal of Computer Vision
Cluster Analysis
International Journal of Computer Vision
Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
A shape descriptors comparison for organs deformation sequence characterization in MRI sequences
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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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.