Authors:
Sebastian Kurtek
1
;
Chafik Samir
2
and
Lemlih Ouchchane
3
Affiliations:
1
The Ohio State University, United States
;
2
Auvergne University, France
;
3
Auvergne University and Clermont University Hospital, France
Keyword(s):
Realistic Simulation, Statistical Modeling, Geodesics, Elastic Deformation, Generalized Cylinders, Reparametrization,
Shape Analysis, Endometriosis, Karcher Mean.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Medical Imaging
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
Abstract:
We propose a new framework for developing statistical shape models of endometrial tissues from real clinical data. Endometrial tissues naturally form cylindrical surfaces, and thus, we adopt, with modification, a recent Riemannian framework for statistical shape analysis of parameterized surfaces. This methodology is based on a representation of surfaces termed square-root normal fields (SRNFs), which enables invariance to all shape preserving transformations including translation, scale, rotation, and re-parameterization. We extend this framework by computing parametrization-invariant statistical summaries of endometrial tissue shapes, and random sampling from learned generative models. Such models are very useful for medical practitioners during different tasks such as diagnosing or monitoring endometriosis. Furthermore, real data in medical applications in general (and in particular in this application) is often scarce, and thus the generated random samples are a key step for eval
uating segmentation and registration approaches. Moreover, this study allows us to efficiently construct a large set of realistic samples that can open new avenues for diagnosing and monitoring complex diseases when using automatic techniques from computer vision, machine learning, etc.
(More)