Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach

  • Andrew GilbertEmail author
  • Marit Holden
  • Line Eikvil
  • Svein Arne Aase
  • Eigil Samset
  • Kristin McLeod
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)


Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to automate 2DE LV measurements. Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations. To improve the network performance we use anatomically meaningful heatmaps as labels and train with a multi-component loss function. Our network achieves 13.4%, 6%, and 10.8% mean percent error on intraventricular septum (IVS), LV internal dimension (LVID), and LV posterior wall (LVPW) measurements respectively. The design outperforms other networks and matches or approaches intra-analyser expert error.


Ultrasound Echocardiography Landmark detection Deep learning Convolutional neural networks 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrew Gilbert
    • 1
    • 2
    Email author
  • Marit Holden
    • 3
  • Line Eikvil
    • 3
  • Svein Arne Aase
    • 1
  • Eigil Samset
    • 1
    • 2
  • Kristin McLeod
    • 1
  1. 1.GE Vingmed Ultrasound, GE HealthcareOsloNorway
  2. 2.Department of InformaticsUniversity of OsloOsloNorway
  3. 3.Norwegian Computing CenterOsloNorway

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