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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

ISBN: 978-989-758-018-5

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 evalu ating 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)

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Paper citation in several formats:
Kurtek S., Samir C. and Ouchchane L. (2014). Statistical Shape Model for Simulation of Realistic Endometrial Tissue.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 421-428. DOI: 10.5220/0004821904210428

@conference{icpram14,
author={Sebastian Kurtek and Chafik Samir and Lemlih Ouchchane},
title={Statistical Shape Model for Simulation of Realistic Endometrial Tissue},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={421-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004821904210428},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Statistical Shape Model for Simulation of Realistic Endometrial Tissue
SN - 978-989-758-018-5
AU - Kurtek S.
AU - Samir C.
AU - Ouchchane L.
PY - 2014
SP - 421
EP - 428
DO - 10.5220/0004821904210428

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