A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations
This work addresses the need for invariant geometric features to analyze organ deformations in medical imaging, though it appears incremental as it builds on existing LDDMM and PDE methods.
The paper tackled the problem of characterizing 3D shapes from point clouds, specifically for analyzing soft tissue organ temporal deformations like bladder motion during forced respiration, and demonstrated robustness on synthetic and MRI data with promising results for applications in medical imaging and other fields.
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.