The diagnosis of many heart-related problems can be done via cardiac function assessment. Expert physicians do perform cardiac function assessment on multiple cardiac cycles. However, such assessment is time-consuming and may be hindered by the variability and accuracy of measurements from cardiac imaging data. Furthermore, although cardiac ultrasound is widely available, inexpensive and safe compared to cardiac CT or MRI, it is operator dependent and hence image quality varies significantly between scans. Therefore, automatic machine learning solutions which rely on using big data to analyze echocardiographic scans to measure important cardiac functions might provide physicians with tools to support their daily clinical routines.
This project aims to study the development of novel machine learning algorithms to automate the segmentation of heart chambers and assess cardiac functions by measuring the left and right ventricle sizes, ejection fraction, and other clinical biometrics. The project also aims to assess myocardial wall motion.