Toward Automated Video Quality Assessment of Ultrasound

Project Context

Traditionally the literature on medical image analysis considers image-based diagnostics (interpreting a captured image). Ultrasound image analysis is different as there is less focus on the image, and more on video processing. Ultrasound imaging is also an interactive real-time imaging technique where video can be cheaply re-taken. Human experts quickly learn how to retake a video if it is sub-optimal or not "fit-for-purpose". Computers are yet to be able to mimic this human capability. This project seeks to investigate deep learning-based video analysis algorithms to advance automated ultrasound video quality assessment towards a generalisable solution more akin to human-like expert behaviour.

Aims and Objectives

We have two large real-world datasets available for this research which will allow us to look at ultrasound video quality assessment from different perspectives. The first is the PULSE dataset which is a large-scale multi-modal freehand datasets of ultrasound video, gaze tracking data, and probe motion data acquired while sonographers perform fetal screening scans. The second dataset is from a basic pregnancy ultrasound study where sonographers have acquired data at two sites following a simple scanning protocol consisting of pre-defined linear sweeps (called the CALOPUS ultrasound protocol (CUP)).

We will use the PULSE multi-modal dataset to study the criteria that human experts use to determine video quality in practice. This will provide insight into the computational criteria that will need to be embedded in a general deep learning model of video QA. We will use the CALOPUS dataset to define the training and test data for the developed video QA models. We will start with simple requirements like "the video is good quality if the fetal head is present". We will then move on to more general requirements such as "the video is good quality if all anatomical structures are clear enough". However, we do not want to build a bespoke solution for every task as this is tedious to do, so our deep learning designs will need to be increasingly "intelligent" and will aim to utilise some of the latest computer vision ideas in their design so that ideally manual annotation is not needed for training (self-supervision), models learn to be generalisable to new unseen tasks (domain adaptation) and they are ideally explainable. The doctoral research will thus progressively explore developing and testing a family of deep learning-based ultrasound video quality assessment methods that are more general.

Novelty and Impact

This research will advance understanding of automated video analysis algorithms with a domain focus on healthcare imaging. Automated ultrasound video quality assessment would be a transformational technology to simplify ultrasound and make it an accessible technology for a wider range of clinical professionals in high-income and low-and-middle-income countries.

Alignment with EPSRC Themes

This project falls within the EPSRC healthcare technologies, ICT and artificial intelligence and robotics research areas

In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.

Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.

Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.

Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.

Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.