Student: Iman Islam
1st Supervisor: Andrew King, King’s College London
2nd Supervisor: Miaojing Shi, King’s College London
Additional Supervisor: Esther Puyol, King’s College London
Clinical Supervisor: Bram Ruijsink, King’s College London
Aim of the PhD Project:
- Develop deep learning techniques for automated interpretation of echocardiography images and train/evaluate them using large-scale datasets.
- Automated characterisation of cardiac function from echocardiography.
- Automated assessment of valvular heart condition and function.
- Machine learning based quality control of model inputs and outputs
Project Description / Background:
Echocardiography is the first port-of-call for assessment and diagnosis of cardiovascular disease (CVD). However, for accurate and robust quantification of many clinical biomarkers cardiac magnetic resonance (CMR) imaging is required. Traditionally, estimating such biomarkers from CMR has required a significant amount of expert interaction, e.g. for contouring the boundaries of the left ventricular myocardium over the cardiac cycle. We have recently published a study that demonstrated that much of this interaction can be avoided using the latest deep learning techniques [1].
Nevertheless, it remains the case that echocardiography is cheaper and more widely available than CMR. In this project we aim to translate, adapt and extend the techniques we have developed for CMR into the realm of echocardiography. This is the right time for such a project – deep learning techniques normally require a large amount of data for training and validation, and recently a number of large-scale databases for echocardiography have become available [2,3]. Furthermore, we have access to thousands of echocardiography scans from the KCL/GSTT Biobank. We believe that our expertise in developing automated machine learning based pipelines for biomarker estimation, and the availability of such datasets, creates an exciting opportunity for high-impact translational research.
Previous work on using deep learning for echocardiography analysis has mainly focused on automated segmentation of the left ventricular (LV) endocardial boundary [4-6] for estimation of ejection fraction (EF). However, boundary identification is prone to errors due to low image quality, the presence of artefacts, and unusual image features linked to different pathologies. As a result, these algorithms can lack robustness. To overcome this limitation, some works have focused on the direct estimation of EF without endocardial border segmentation [7-8]. Although this solution could be more reliable, it is less interpretable and more difficult for clinicians to assess the accuracy of the results.
We propose to develop machine and deep learning-based techniques for automated quantification of echocardiography scans. We will investigate the use of transfer learning from our CMR-based models, and design domain adaption techniques to take advantage of our established knowledge in this new task. Furthermore, we will seek to go beyond the estimation of simple metrics such as end-diastolic and end-systolic volumes and EF, to paint a much richer picture of the heart in health and disease.
In addition, echocardiography remains the first line technique for assessing valvular heart disease and regurgitation due to its excellent visualisation of the valve leaflets, which are not visible in CMR. Part of this project will focus on the automated assessment of the anatomy and function of the different heart valves. With appropriate quality control tools and confidence measures, the techniques could, in principle, work in milliseconds and give the sonographer real-time feedback whilst scanning. The subsequent analysis of the estimated biomarkers at scale could enable interesting and valuable research into the nature of CVD and the progression of the heart into disease.
Candidates for this project would be expected to have a strong background in computer programming, and either experience in, or a desire to learn about machine and deep learning techniques applied to medical images.
An illustration of a pipeline for automated quantification of cardiac function from echocardiography images.
References:
- B. Ruijsink, et al, Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function, JACC: Cardiovascular Imaging, 13(3):684-695, 2020.
- Echonet Dynamic – URL: https://echonet.github.io/dynamic/
- CAMUS – Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation – URL: https://www.creatis.insa-lyon.fr/Challenge/camus/
- J. Zhang et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation, 138.16:1623-1635, 2018.
- D. Ouyang et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature, 580.7802 (2020): 252-256.
- S. Leclerc et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE transactions on medical imaging 38.9:2198-2210, 2019
- A. Ghorbani et al. Deep learning interpretation of echocardiograms. NPJ digital medicine 3.1:1-10, 2020.
- F. M. Asch et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circulation: Cardiovascular Imaging, 12.9:e009303, 2019.