Machine Learning and Medical Image Analysis for Point-of-Care Ultrasound Systems

Lead Research Organisation: University of Oxford
Department Name: Engineering Science


Access to diagnostic ultrasound (US) in low and middle income countries (LMICs) is impeded because of a lack of experienced sonographers and the expertise required to scan well. Furthermore, conventional US systems carry the burden of high equipment costs reducing their feasibility for use in such environments. Statistics show that worldwide, 99% of maternal deaths occur in LMICs indicating an important unmet clinical need to provide US-based diagnosis [1]. Recent state-of-the-art advances have engineered low-cost US devices which show high potential for use in point-of-care (POC) scenarios. Similarly, advancements in machine learning architectures now offer superior performance over predicate solutions and open realms of possibility in computer pattern recognition of 2D US images and video. There is a real potential for automated computer analysis of US to be deployed within POC US systems in countries such as India as well as in Africa, thus addressing the skills crisis and diagnostic need.

The proposed doctoral research aims to develop and evaluate a novel automated image analysis framework that utilises a simplified US scanning protocol of multiple scanning sweeps to provide a clinical decision support tool for healthcare workers unfamiliar with ultrasound. 2D US images and video are rich in spatial-temporal features and acoustic patterns and the research will consider how these can be extracted and combined to good effect within machine learning architectures to reveal important pieces of clinical information. A first key challenge of this project will be to translate the clinical criteria, obtained from the literature and clinical collaborators, into a machine learning framework. A second challenge will be how to design and implement appropriate machine learning architecture for the chosen clinical tasks. A deep learning approach is most likely to be a feasible method. Finally, feasibility studies will be performed in collaboration with clinical partners in the UK and India to evaluate the developed methods and consider the potential usability in practice.

The research falls within the EPSRC's 'Healthcare Technologies' and 'Engineering' research themes. In particular, the research develops work within 'Image and Vision Computing', 'Human-Computer Interaction', and 'Medical Imaging' sub-themes. The research also fits into the EPSRC Grand Challenges through 'Transforming Community Health and Care' and 'Optimising Treatment'.

The doctoral research will be conducted associated with the GCRF-funded CALOPUS Project (Computer-Assisted Low-cost Point-of-Care Ultrasound: EP/R013853/1) which is a joint collaboration between the Institute of Biomedical Engineering (IBME) and Nuffield Department of Women's and Reproductive Health, University of Oxford, and the Translational Health Science and Technology Institute (THSTI), Faridabad, India.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513295/1 30/09/2018 29/09/2023
2288295 Studentship EP/R513295/1 30/09/2019 30/03/2023 Alexander Darius Gleed
Description We have explored ways in which a user unfamiliar with ultrasound can assess the placenta location. We have built an automatic tool that uses machine learning to identify and label the placenta and maternal bladder in ultrasound video. The ultrasound video is obtained by a user who takes a simple U-shaped video sweep low across the maternal abdomen. The tool automatically provides an assistive video overlay which aids a user in assessing the placenta location by highlighting key anatomies and landmarks. This may aid a user in stratifying placentas which are low, thus enabling women at risk to seek the appropriate level of care. In the process of building this tool, we have explored the shape of the placenta and its implications with respect to the U-shaped video sweep and the machine learning algorithm. We have also explored how to translate the clinical assessment of a placenta which is low to a suitable automatic machine learning approach. In this work, we have developed collaborations with our partner institute abroad, including evaluation of the tool on their video data, and sharing expertise and knowledge through technical meetings.

The award objectives were to explore and develop ways in which ultrasound can be simplified, with the aid of clinical ultrasound video sweep protocols and machine learning algorithms. Our work thus far has been successful in this regard as we have built a tool which uses a ultrasound video sweep input and automatically produces an assistive video overlay. This dramatically simplifies the process of assessing the placenta location, for a user unfamiliar in ultrasound. A future direction of this work is to fully-automate the assessment process. We have discovered that this requires automatic (machine) recognition of the cervix, an anatomical structure which is small and challenging to identify. Finally, this doctoral fund has contributed to the high-quality training of future research workforce.

The doctoral research contributed to a publication:
Exploitation Route Our work is of interest to any groups that are interested in simplifying the use of ultrasound. For example, there are several academic research groups worldwide who are investigating simplifying the use of ultrasound, in addition to partnership with industrial groups that provide ultrasound products. There are also a number of industry groups which design and sell products which simplify the use of ultrasound.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare

Description Interdisciplinary working with THSTI (India) through the CALOPUS Project 
Organisation Translational Health Science And Technology Institute
Country India 
Sector Public 
PI Contribution I have participated (as a doctoral research student) in the interdisciplinary study of the CALOPUS project, which is a partnership between the University of Oxford, UK (Alison Noble and Aris Papageorghiou) and Translational Health Science and Technology Institute, India (Shinjini Bhatnagar). I have addressed a research problem within this project, looking at automatic assessment of the placenta location from ultrasound video. I have built a tool which may simplify the assessment of the placenta location in ultrasound video. I have provided support to team members at the THSTI side regarding the properties of the video data and clinical annotations we have collected at the Oxford side.
Collaborator Contribution THSTI have provided clinical video data which has been used in the evaluation of the tool I have developed. They performed the evaluation of the tool at their site using (offline) ultrasound video data and reported results back to the Oxford side.
Impact We have published a clinical abstract in ISUOG 2021.
Start Year 2019