AI-Enabled Assessment of Cardiac Function from Echocardiography

PHD项目的目标：开发用于自动解释超声心动图图像的深度学习技术，并使用大规模数据集对其进行训练/评估。从超声心动图自动确定心脏功能的特征。自动评估心脏瓣膜状况和功能。基于机器学习的模型输入和输出质量控制项目描述：超声心动图是评估和诊断心血管疾病(CVD)的第一工具。然而，为了准确和稳健地量化许多临床生物标志物，心脏磁共振(CMR)成像是必需的。传统上，从CMR估计这样的生物标志物需要大量的专家交互，例如在心动周期中勾勒出左室心肌的边界。我们最近发表的一项研究表明，使用最新的深度学习技术可以避免许多这种交互[1]。然而，超声心动图比CMR更便宜、更广泛地获得。在这个项目中，我们的目标是将我们为CMR开发的技术转换、调整和扩展到超声心动图领域。这是这样一个项目的合适时机-深度学习技术通常需要大量数据来进行训练和验证，最近一些用于超声心动图的大型数据库已经可用[2，3]。此外，我们还可以从KCL/GSTT生物库获得数千例超声心动图扫描。我们相信，我们在开发用于生物标志物评估的基于自动机器学习的管道方面的专业知识，以及此类数据集的可用性，为高影响力的翻译研究创造了一个令人兴奋的机会。以前使用深度学习进行超声心动图分析的工作主要集中在自动分割左心室(LV)心内膜边界[4-6]以估计射血分数(EF)。然而，由于图像质量低、伪影的存在以及与不同病理相关的异常图像特征的存在，边界识别容易出错。因此，这些算法可能缺乏健壮性。为了克服这一局限性，一些工作集中在不需要心内膜边界分割的情况下直接估计EF[7-8]。虽然这种解决方案可能更可靠，但它更难解释，临床医生更难评估结果的准确性。我们建议开发基于机器和深度学习的技术，用于超声心动图扫描的自动量化。我们将研究基于CMR的模型中迁移学习的使用，并设计领域适应技术，以在这项新任务中利用我们已有的知识。此外，我们将寻求超越简单的指标，如舒张末和收缩末期容量和EF的估计，以描绘更丰富的心脏健康和疾病的图景。此外，超声心动图仍然是评估瓣膜心脏病和反流的第一线技术，因为它对瓣膜叶的良好可视化，这在CMR中是看不到的。该项目的一部分将侧重于对不同心脏瓣膜的解剖和功能进行自动评估。有了适当的质量控制工具和置信度措施，这些技术原则上可以在毫秒内发挥作用，并在扫描时为超声诊断员提供实时反馈。随后对估计的生物标志物进行规模分析，可以对心血管疾病的性质和心脏疾病的进展进行有趣和有价值的研究。

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 outputsProject Description: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.