Literature DB >> 24561989

Whole myocardium tracking in 2D-echocardiography in multiple orientations using a motion constrained level-set.

T Dietenbeck1, D Barbosa2, M Alessandrini3, R Jasaityte4, V Robesyn4, J D'hooge4, D Friboulet3, O Bernard3.   

Abstract

The segmentation and tracking of the myocardium in echocardiographic sequences is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we extend a level-set method recently proposed in Dietenbeck et al. (2012) in order to track the whole myocardium in echocardiographic sequences. To this end, we enforce temporal coherence by adding a new motion prior energy to the existing framework. This motion prior term is expressed as new constraint that enforces the conservation of the levels of the implicit function along the image sequence. Moreover, the robustness of the proposed method is improved by adjusting the associated hyperparameters in a spatially adaptive way, using the available strong a priori about the echocardiographic regions to be segmented. The accuracy and robustness of the proposed method is evaluated by comparing the obtained segmentation with experts references and to another state-of-the-art method on a dataset of 15 sequences (≃ 900 images) acquired in three echocardiographic views. We show that the algorithm provides results that are consistent with the inter-observer variability and outperforms the state-of-the-art method. We also carry out a complete study on the influence of the parameters settings. The obtained results demonstrate the stability of our method according to those values.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Active contour; Echocardiography; Segmentation; Tracking

Mesh:

Year:  2014        PMID: 24561989     DOI: 10.1016/j.media.2014.01.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  3 in total

1.  Automatic cardiac evaluations using a deep video object segmentation network.

Authors:  Nasim Sirjani; Shakiba Moradi; Mostafa Ghelich Oghli; Ali Hosseinsabet; Azin Alizadehasl; Mona Yadollahi; Isaac Shiri; Ali Shabanzadeh
Journal:  Insights Imaging       Date:  2022-04-08

2.  Comparative studies of deep learning segmentation models for left ventricle segmentation.

Authors:  Muhammad Ali Shoaib; Khin Wee Lai; Joon Huang Chuah; Yan Chai Hum; Raza Ali; Samiappan Dhanalakshmi; Huanhuan Wang; Xiang Wu
Journal:  Front Public Health       Date:  2022-08-25

3.  A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography.

Authors:  Suyu Dong; Gongning Luo; Kuanquan Wang; Shaodong Cao; Qince Li; Henggui Zhang
Journal:  Biomed Res Int       Date:  2018-09-10       Impact factor: 3.411

  3 in total

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