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Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model

  • Yuanwei Li
  • Chin Pang Ho
  • Navtej Chahal
  • Roxy Senior
  • Meng-Xing TangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent enables myocardial perfusion quantification which is invaluable for the early detection of coronary artery diseases. In this paper, we proposed a new segmentation method called Shape Model guided Random Forests (SMRF) for the analysis of MCE data. The proposed method utilizes a statistical shape model of the myocardium to guide the Random Forest (RF) segmentation in two ways. First, we introduce a novel Shape Model (SM) feature which captures the global structure and shape of the myocardium to produce a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to further refine and constrain the final segmentation to plausible myocardial shapes. Evaluated on clinical MCE images from 15 patients, our method obtained promising results (Dice = 0.81, Jaccard = 0.70, MAD = 1.68 mm, HD = 6.53 mm) and showed a notable improvement in segmentation accuracy over the classic RF and its variants.

Notes

Acknowledgments

The authors would like to thank Prof. Daniel Rueckert, Liang Chen and other members from the BioMedIA group for their help and advice. This work was supported by the Imperial College PhD Scholarship.

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Yuanwei Li
    • 1
  • Chin Pang Ho
    • 2
  • Navtej Chahal
    • 3
  • Roxy Senior
    • 3
  • Meng-Xing Tang
    • 1
    Email author
  1. 1.Department of BioengineeringImperial College LondonLondonUK
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Department of EchocardiographyRoyal Brompton HospitalLondonUK

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