Statistical model for simulation of deformable elastic endometrial tissue shapes

NEUROCOMPUTING

卷:173

Article

Kurtek, Sebastian¹  Xie, Qian²  Samir, Chafik³  Canis, Michel³ 

[1] Ohio State Univ, Dept Stat, Columbus, OH 43210 USA

[2] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA

[3] Univ Clermont, CNRS, ISIT UMR UdA 6284, Clermont Ferrand, France

关键词: Elastic deformation;    Endometriosis;    Simulation;    Statistical shape model;   

DOI  :  10.1016/j.neucom.2015.03.098

来源: Elsevier

【 摘 要 】

Statistical shape analysis plays a key role in various medical imaging applications. Such methods provide tools for registering, deforming, comparing, averaging, and modeling anatomical shapes. In this work, we focus on the application of a recent method for statistical shape analysis of parameterized surfaces to simulation of endometrial tissue shapes. The clinical data contains magnetic resonance imaging (MRI) endometrial tissue surfaces, which are used to learn a generative shape model. We generate random tissue shapes from this model, and apply elastic semi-synthetic deformations to them. This provides two types of simulated data: (1) MM-type (without deformation) and (2) transvaginal ultrasound (TVUS)-type, which undergo an additional deformation due to the transducer's pressure. The proposed models can be used for validation of automatic, multimodal image registration, which is a crucial step in diagnosing endornetriosis. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free