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Myocardium segmentation from DE MRI with guided random walks and sparse shape representation

  • Jie Liu
  • Xiahai Zhuang
  • Hongzhi Xie
  • Shuyang Zhang
  • Lixu Gu
Original Article

Abstract

Purpose

For patients with myocardial infarction (MI), delayed enhancement (DE) cardiovascular magnetic resonance imaging (MRI) is a sensitive and well-validated technique for the detection and visualization of MI. The myocardium viability assessment with DE MRI is important in diagnosis and treatment management, where myocardium segmentation is a prerequisite. However, few academic works have focused on automated myocardium segmentation from DE images. In this study, we aim to develop an automatic myocardium segmentation algorithm that targets DE images.

Methods

We propose a segmentation framework based on both prior shape knowledge and image intensity. Instead of the strong request of the pre-segmentation of cine MRI in the same session, we use the sparse representation method to model the myocardium shape. Data from the Cardiac MR Left Ventricle Segmentation Challenge (2009) are used to build the shape template repository. The method of guided random walks is used to integrate the shape model and intensity information. An iterative approach is used to gradually improve the results.

Results

The proposed method was tested on the DE MRI data from 30 MI patients. The proposed method achieved Dice similarity coefficients (DSC) of 74.60 ± 7.79% with 201 shape templates and 73.56 ± 6.32% with 56 shape templates, which were close to the inter-observer difference (73.94 ± 5.12%). To test the generalization of the proposed method to routine clinical images, the DE images of 10 successive new patients were collected, which were unseen during the method development and parameter tuning, and a DSC of 76.02 ± 7.43% was achieved.

Conclusion

The authors propose a novel approach for the segmentation of myocardium from DE MRI by using the sparse representation-based shape model and guided random walks. The sparse representation method effectively models the prior shape with a small number of shape templates, and the proposed method has the potential to achieve clinically relevant results.

Keywords

Delayed enhancement MRI Myocardium segmentation Guided random walks Prior shape modeling Sparse representation 

Notes

Acknowledgements

This research is supported by the National Key Research and Development Program (2016YFC0106200), the 863 National Research Fund (2015AA043203), the Science and Technology Commission of Shanghai Municipality (17JC1401600) and the National Nature Science Foundation of China (61190120, 61190124, 61271318, 81301283 and 81511130090).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study formal consent is not required. The testing data were collected at our institution with approval from the institutional review board.

Informed consent

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Data ScienceFundan UniversityShanghaiChina
  3. 3.Department of Cardiothoracic SurgeryPeking Union Medical College HospitalBeijingChina

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