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Segmentation of the left ventricle using distance regularized two-layer level set approach.

Chaolu Feng1, Chunming Li2, Dazhe Zhao1

  • 1Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, LiaoNing 110819, China.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 8, 2014
PubMed
Summary

This study introduces a novel two-layer level set method for segmenting the left ventricle (LV) in cardiac magnetic resonance (CMR) images. The approach enhances segmentation accuracy and anatomical consistency by preserving the distance between endocardial and epicardial contours.

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Area of Science:

  • Medical Imaging
  • Image Analysis
  • Computational Anatomy

Background:

  • Accurate segmentation of the left ventricle (LV) from cardiac magnetic resonance (CMR) images is crucial for diagnosing cardiovascular diseases.
  • Existing segmentation methods often struggle with intensity inhomogeneities and maintaining anatomical consistency.

Purpose of the Study:

  • To develop a novel two-layer level set approach for robust and accurate LV segmentation from CMR short-axis images.
  • To improve the preservation of anatomical geometry between the endocardium and epicardium during segmentation.

Main Methods:

  • A two-layer level set method representing endocardium and epicardium with distinct level contours.
  • Incorporation of a distance regularization (DR) constraint to maintain consistent distance between contours.
  • Utilizing a data term based on local intensity clustering to overcome intensity inhomogeneities.

Main Results:

  • The proposed method achieved high segmentation accuracy on MICCAI grand challenge datasets.
  • Demonstrated superior consistency with anatomical geometry compared to existing methods.
  • Effectively handled intensity inhomogeneities present in CMR images.

Conclusions:

  • The novel two-layer level set approach provides accurate and anatomically consistent segmentation of the left ventricle.
  • The distance regularization constraint is key to preserving the geometric integrity of the LV.
  • This method offers a significant advancement for quantitative analysis of cardiac magnetic resonance imaging.