Literature DB >> 25571046

A novel level set method for segmentation of left and right ventricles from cardiac MR images.

Yu Liu, Chunming Li, Shuxu Guo, Yihua Song, Yue Zhao.   

Abstract

In this paper, we propose a novel level set method for segmentation of cardiac left and right ventricles based on the distance regularized level set evolution (DRLSE) framework [7] and the distance regularized two-layer level set (DR2LS) model [17]. First, DRLSE is applied to obtain a preliminary segmentation of left and right ventricles, which is then used to initialize the endocardial contour, which is represented by the zero level contour of the level set function in our method. Then, the epi-cardial contour is represented by a different level contour of the same level set function. These two level sets are optimized by an energy minimization process to best fit the true endocardium and epicardium. In order to ensure smoothly varying distance between the two level contours, we introduce a distance regularization constraint in the energy function. With the region-scalable fitting (RSF) energy [8] as the data term, our method is able to deal with intensity inhomogeneities in the images, which is a main source of difficulty in image segmentation. Our method has been tested on cardiac MR images with promising results.

Mesh:

Year:  2014        PMID: 25571046     DOI: 10.1109/EMBC.2014.6944678

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Left ventricle: fully automated segmentation based on spatiotemporal continuity and myocardium information in cine cardiac magnetic resonance imaging (LV-FAST).

Authors:  Lijia Wang; Mengchao Pei; Noel C F Codella; Minisha Kochar; Jonathan W Weinsaft; Jianqi Li; Martin R Prince; Yi Wang
Journal:  Biomed Res Int       Date:  2015-02-08       Impact factor: 3.411

2.  Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours.

Authors:  Shafiullah Soomro; Farhan Akram; Asad Munir; Chang Ha Lee; Kwang Nam Choi
Journal:  Comput Math Methods Med       Date:  2017-08-08       Impact factor: 2.238

3.  N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation.

Authors:  Xingqing Nie; Xiaogen Zhou; Tong Tong; Xingtao Lin; Luoyan Wang; Haonan Zheng; Jing Li; Ensheng Xue; Shun Chen; Meijuan Zheng; Cong Chen; Min Du
Journal:  Front Neurosci       Date:  2022-09-01       Impact factor: 5.152

  3 in total

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