Statistical Shape Model for Simulation of Realistic Endometrial Tissue

Sebastian Kurtek, Chafik Samir, Lemlih Ouchchane

2014

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 evaluating 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.

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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