View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning
This enables automatic extraction of heart function measurements for clinicians, but it is incremental as it builds on prior segmentation-based detection methods.
The study tackled automated localization and classification of heart valves in echocardiograms using a deep learning pipeline, achieving precise localization and identification of multiple valves in Apical views.
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.