View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning

TL;DR

This paper presents a deep learning pipeline for classifying echocardiogram views and detecting heart valves, enabling automated analysis of cardiac function from ultrasound images.

Contribution

It introduces a novel approach combining view classification and object detection to localize and identify valves in 2D ultrasound images, a first in this context.

Findings

Accurate localization and classification of valves in echocardiograms.

First use of deep neural networks for bounding box prediction of valves.

Demonstrated effectiveness on Apical view images.

Abstract

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.