Direct Estimation of Cardiac Bi-ventricular Volumes with Regression Forests

  • Xiantong Zhen
  • Zhijie Wang
  • Ali Islam
  • Mousumi Bhaduri
  • Ian Chan
  • Shuo Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without segmentation and user inputs. Based on the cardiac image representation by multiple and complementary features, we adopt regression forests to jointly estimate the two volumes. Our method is validated on a dataset of 56 subjects with a total of 3360 MR images which shows that our method can achieve a high correlation coefficient of around 0.9 with manual segmentation obtained by human experts. With our proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.


Random Forest Right Ventricle Direct Estimation Manual Segmentation Split Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiantong Zhen
    • 1
  • Zhijie Wang
    • 1
  • Ali Islam
    • 3
  • Mousumi Bhaduri
    • 4
  • Ian Chan
    • 4
  • Shuo Li
    • 1
    • 2
  1. 1.The University of Western OntarioLondonCanada
  2. 2.GE HealthcareLondonCanada
  3. 3.St. Joseph’s Health CareLondonCanada
  4. 4.London Health Sciences CentreCanada

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