Fully automatic segmentation of 4D MRI for cardiac functional measurements

Yan Wang, Yue Zhang, Wanling Xuan, Evan Kao, Peng Cao, Bing Tian, Karen Ordovas, David Saloner, Jing Liu

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Purpose: Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three-dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four-dimensional, 4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D + t) cardiac MR images. Methods: The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary. Results: This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62 ± 5.47%, 87.35 ± 7.26%, and 82.63 ± 6.22% for the LV endocardial, LV epicardial, and RV contours, respectively. Conclusions: We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.

Original languageEnglish (US)
Pages (from-to)180-189
Number of pages10
JournalMedical Physics
Issue number1
StatePublished - Jan 2019
Externally publishedYes


  • left ventricle
  • level set method
  • right ventricle
  • segmentation

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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