Toward Automated Video Quality Assessment of Ultrasound

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Project ContextTraditionally 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 ObjectivesWe 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 ImpactThis 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 ThemesThis project falls within the EPSRC healthcare technologies, ICT and artificial intelligence and robotics research areas