View Classification and Object Detection in Cardiac Ultrasound to
  Localize Valves via Deep Learning
Derya Gol Gungor Bimba Rao Cynthia Wolverton Ismayil Guracar
Echocardiography provides an important tool for clinicians to observe the
function of the heart in real time, at low cost, and without harmful radiation.
Automated localization and classification of heart valves enables automatic
extraction of quantities associated with heart mechanical function and related
blood flow measurements. We propose a machine learning pipeline that uses deep
neural networks for separate classification and localization steps. As the
first step in the pipeline, we apply view classification to echocardiograms
with ten unique anatomic views of the heart. In the second step, we apply deep
learning-based object detection to both localize and identify the valves. Image
segmentation based object detection in echocardiography has been shown in many
earlier studies but, to the best of our knowledge, this is the first study that
predicts the bounding boxes around the valves along with classification from 2D
ultrasound images with the help of deep neural networks. Our object detection
experiments applied to the Apical views suggest that it is possible to localize
and identify multiple valves precisely.