Literature DB >> 25444692

Semi-automated segmentation and quantification of mitral annulus and leaflets from transesophageal 3-D echocardiographic images.

Miguel Sotaquira1, Mauro Pepi2, Laura Fusini2, Francesco Maffessanti3, Roberto M Lang4, Enrico G Caiani5.   

Abstract

Quantification of three-dimensional (3-D) morphology of the mitral valve (MV) using real-time 3-D transesophageal echocardiography (RT3-D TEE) has proved to be a valuable tool for the assessment of MV pathologies, but of limited use in clinical practice because it relies on user-intensive approaches. This study presents a new algorithm for the segmentation and morphologic quantification of the mitral annulus (MA) and mitral leaflets (ML) in closed valve configuration from RT3-D TEE volumes. Following initialization, the MA and the ML and the coaptation line (CL) are automatically obtained in 3-D. Validation with manual tracings was performed on 33 patients, resulting in segmentation errors in the order of 0.7 mm and 0.6 mm for the MA and ML segmentation, in addition to good intra- and inter-observer reproducibility (coefficients of variation below 12% and 15%, respectively). The ability of the algorithm to assess different MV pathologies as well as repaired valves with implanted annular rings was also explored. The reported performance of the proposed fast, semi-automated MA and ML quantification makes it promising for future applications in clinical settings such as the operating room, where obtaining results in short time is important.
Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Block-matching; Echocardiography; Graph-based segmentation; Mitral annulus; Mitral leaflets; Mitral valve; Mitral valve quantification

Mesh:

Year:  2014        PMID: 25444692     DOI: 10.1016/j.ultrasmedbio.2014.09.001

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  3 in total

1.  Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling.

Authors:  Wenyao Xia; John Moore; Elvis C S Chen; Yuanwei Xu; Olivia Ginty; Daniel Bainbridge; Terry M Peters
Journal:  J Med Imaging (Bellingham)       Date:  2018-02-09

2.  Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences.

Authors:  Lennart Tautz; Lars Walczak; Joachim Georgii; Amer Jazaerli; Katharina Vellguth; Isaac Wamala; Simon Sündermann; Volkmar Falk; Anja Hennemuth
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-10-09       Impact factor: 2.924

3.  Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning.

Authors:  Christian Herz; Danielle F Pace; Hannah H Nam; Andras Lasso; Patrick Dinh; Maura Flynn; Alana Cianciulli; Polina Golland; Matthew A Jolley
Journal:  Front Cardiovasc Med       Date:  2021-12-09
  3 in total

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