Literature DB >> 24094590

Automatic segmentation of cardiac MRI cines validated for long axis views.

Yossi Tsadok1, Yael Petrank, Sebastian Sarvari, Thor Edvardsen, Dan Adam.   

Abstract

Segmentation of cardiac magnetic resonance imaging is considered an important application in clinical practice. An automatic algorithm is proposed for segmentation of both endocardial and epicardial boundaries, in long-axis views. The data consisted of 126 patients, yielding 1008 traces. Estimated clinical parameters were highly correlated to gold standard measurements. The error between the automatic tracing and the gold standard was not significantly different than the error between two manual observers. In conclusion, a tool for segmenting the myocardial boundaries in the long-axis views is proposed, which works well, as demonstrated by the validation performed using a clinical dataset.
Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Cardiac magnetic resonance imaging (cMRI); Image registration; Image segmentation; Long-axis views; Myocardial segmentation

Mesh:

Year:  2013        PMID: 24094590     DOI: 10.1016/j.compmedimag.2013.09.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  3 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

2.  A 3D Hermite-based multiscale local active contour method with elliptical shape constraints for segmentation of cardiac MR and CT volumes.

Authors:  Leiner Barba-J; Boris Escalante-Ramírez; Enrique Vallejo Venegas; Fernando Arámbula Cosío
Journal:  Med Biol Eng Comput       Date:  2017-10-23       Impact factor: 2.602

Review 3.  Artificial intelligence and cardiovascular imaging: A win-win combination.

Authors:  Luigi P Badano; Daria M Keller; Denisa Muraru; Camilla Torlasco; Gianfranco Parati
Journal:  Anatol J Cardiol       Date:  2020-10       Impact factor: 1.596

  3 in total

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