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

Rahim, Mehdi; Bellemare, Marc‐Emmanuel; Bulot, Rémy; Pirrò, N.

doi:10.1007/s10851-012-0391-6

Cited by 8 publications

(13 citation statements)

References 35 publications

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“…Point cloud tracking has led to many significant advances in many computer vision applications such as robotics [25], astronomy [10] and medical imaging [26]. A point cloud based dynamical system modulation for reactive avoidance of concave and convex obstacles during robot motion is presented in [28].…”

Section: Point Cloud Trackingmentioning

confidence: 99%

“…However, dynamic MRI suffers from its low-resolution in addition to its sensitivity to motion artifacts. A diffeomorphic mapping based characterization of temporal sequences is first presented in [26] to quantify the temporal deformation of the pelvic floor organs in 2D+t. However, a more informative 3D+t quantification of the pelvic floor organ dynamics is necessary because of the large shape variability of the pelvic organs across time.…”

Section: Introductionmentioning

confidence: 99%

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A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

Makki¹,

Bohi²,

Ogier³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of a bladder during a forced respiratory motion with a reduced number of 3D surface points: first, a set of equidistant points representing the vertices of quadrilateral mesh for the surface in the first time frame are tracked throughout a long dynamic MRI sequence using a Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. Second, a novel geometric feature which is invariant to scaling and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic MRI data portraying the bladder deformation during forced respiratory motions. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

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Section: Point Cloud Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

Makki¹,

Bohi²,

Ogier³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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show abstract

“…Shape elongations and distortions have been used in medical imaging, in particular to characterize the deformations of the pelvic organs in the plane from dynamical organ contour points [34]. However, 2D+t shape processing can result in biased estimation of common measures because of the out-of-plane problem.…”

Section: Mesh Elongation and Distortionmentioning

confidence: 99%

“…-Biomechanical descriptors : the deformations are quantified using mesh temporal elongations and distortions. This aimed to extend the methodologies used in [34] from 2D+t to 3D+t. -Geometric descriptors (family of geodesic-based features) : which are the mean curvature and the new proposed feature.…”

Section: Geometric Descriptorsmentioning

confidence: 99%

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

Makki¹,

Bohi²,

Ogier³

et al. 2020

Preprint

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In this work, we present a pipeline for characterization of bladder surface dynamics during deep respiratory movements from dynamic Magnetic Resonance Imaging (MRI). Dynamic MRI may capture temporal anatomical changes in soft tissue organs with high-contrast but the obtained sequences usually suffer from limited volume coverage which makes the high resolution reconstruction of organ shape trajectories a major challenge in temporal studies. For a compact shape representation, the reconstructed temporal data with full volume coverage are first used to establish a subject-specific dynamical 4D mesh sequences using the large deformation diffeomorphic metric mapping (LDDMM) framework. Then, we performed a statistical characterization of organ shape changes from mechanical parameters such as mesh elongations and distortions. Since shape space is curved, we have also used the intrinsic curvature changes as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we propose a non-parametric level set method to evaluate spatio-temporal surface evolution. Independent of parameterization and minimizing the length of the geodesic curves, it shrinks smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used for evaluation of surface dynamics from the curve-shortening flow. Results demonstrate the numerical stability of the derived descriptor throughout smooth continuous-time organ trajectories. Intercorrelations between individuals' motion patterns from different geometric features are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping.

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“…In Ref. 28, landmarks are tracked over the boundary of pelvic organs during strain for prolapse analysis. Another work focused on generating biomechanical models to simulate pelvic organ movement.…”

Section: Related Workmentioning

confidence: 99%

Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI

et al. 2018

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A method is presented to automatically track and segment pelvic organs on dynamic magnetic resonance imaging (MRI) followed by multiple-object trajectory classification to improve understanding of pelvic organ prolapse (POP). POP is a major health problem in women where pelvic floor organs fall from their normal position and bulge into the vagina. Dynamic MRI is presently used to analyze the organs' movements, providing complementary support for clinical examination. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In the proposed method, organs are first tracked and segmented using particle filters and [Formula: see text]-means clustering with prior information. Then, the trajectories of the pelvic organs are modeled using a coupled switched hidden Markov model to classify the severity of POP. Results demonstrate that the presented method can automatically track and segment pelvic organs with a Dice similarity index above 78% and Hausdorff distance of [Formula: see text] for 94 tested cases while demonstrating correlation between organ movement and POP. This work aims to enable automatic tracking and analysis of multiple deformable structures from images to improve understanding of medical disorders.

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

Cited by 8 publications

References 35 publications

A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI

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