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Application and development of machine learning approaches for automated analysis of Paediatric Brain Tumours


Project Description

There is significant interest in the use of image processing techniques and machine learning to improve automation and facilitate clinical decision making.

With our partners at Alder Hey Children’s Hospital, a Centre of Excellence in Cancer Treatment, this PhD project aims to automate the measurement of children’s brain tumours based on images of the brain produced by scanning machines such as Magnetic Resonance Imaging (MRI).

It is important to be able to accurately measure the size of a tumour over time when evaluating disease progression and informing treatment, but is typically a time-consuming process for clinicians and difficult to accurately reproduce.

There is much interest in using Artificial Intelligence (AI) / Machine Learning techniques to automatically recognise brain tumours within MRI images [1], but this is still a rapidly developing field which has primarily been focused on data from adults. Brain tumours in children are strikingly different to those in adults [2], and algorithms will require specialised training to identify the boundaries of different types of tumour, and how their volumes are changing over time, when the brain may also be simultaneously developing and growing rapidly.
One commonly used technique is to apply AI algorithms, such as Convolutional Neural Networks, which are given a set of ’training’ data - existing tumour scans which have been manually annotated by experts - and can automatically derive a computational model to classify new data. We anticipate that similar approaches can be used, making use of the image data and expert analysis provided by Alder Hey.
In this project, the student will research and develop the software, hardware, and network infrastructures that will make it possible to automate the process of measuring brain tumours from MRI data so that it can be used directly by clinicians at Alder Hey. This will involve being able to successfully research, apply and optimise existing AI solutions to this new data, as well as developing new software that will allow the data collection, cleaning, and transfer processes to be automated.

Candidates should be competent in coding (e.g. python, R or equivalent suitable language), and have interest / expertise in data science methods applied to medical research. Alongside the Department of Computer Science, supervisors from the Computational Biology Facility and Alder Hey Children’s Hospital will provide support in the theoretical and applied aspects of developing this project, and appropriate training will be provided in the methods and medical terminology as needed.

The supervisory team comprises:
Dr Martin Gairing, (Department of Computer Science, University of Liverpool, https://cgi.csc.liv.ac.uk/~gairing/). Research interests: algorithms, optimisation, game theory, machine learning.
Prof Andy Jones (Institute of Integrative Biology and Director Computational Biology Facility, https://www.liverpool.ac.uk/computational-biology-facility/ ; Twitter: @andy___jones; – https://www.liverpool.ac.uk/integrative-biology/staff/andrew-jones/. Research interests: “omics” data mining, machine learning, bioinformatics software development.
Dr Shivaram Avula (Consultant Radiologist, Alder Hey Children’s NHS Foundation Trust) Research interests: paediatric brain tumours, cerebellar mutism syndrome and advanced neuroimaging.
Dr Antony McCabe Computational Biology Facility, https://www.liverpool.ac.uk/computational-biology-facility/ Research interests: bioinformatics software development, machine learning.
For application enquires please contact Dr Antony McCabe ().
The University of Liverpool Doctoral Network in Artificial Intelligence (AI) for Future Digital Health aims to create and maintain a community of AI health care professionals that can develop and apply AI research to medical problems, see https://www.liverpool.ac.uk/study/postgraduate-research/doctoral-training-programmes/ai-for-future-digital-health.

The vision is to provide a high-quality doctoral training within the broad domain of AI (including Machine Learning, Data Science and Statistics) for medical applications from health care to drug design. The weekly 3-hour training sessions include various topics from Statistics and Linear Algebra to guest lectures on AI and healthcare, see http://kurlin.org/doctoral-network.php#training. New students starting in October 2020 will join our first cohort of 8 PhD students who have started in October 2019.

Each PhD project has been carefully co-created in collaboration with a health care provider and/or a commercial partner working with medical data so that the outcomes of the PhD research will have immediate benefit. The network will provide students with regular training and internship opportunities at industry partners. Applications are welcome from enthusiastic candidates with at least a 2:1 degree in Computer Science, Engineering, Mathematics, Bioinformatics or a similar area, with an interest in developing data science approaches for life sciences or healthcare.

To apply for this opportunity, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ ’Applications should be made to a PhD in Computer Science.

Funding Notes

This project is funded by the University of Liverpool through the doctoral network AI for Future Digital Health, View Website. The funding is restricted to UK students.

References

[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp1993-2024.
[2] Thomas E. Merchant, Ian F. Pollack, Jay S. Loeffler. (2010). Brain Tumors Across the Age Spectrum: Biology, Therapy, and Late Effects. Seminars in Radiation Oncology,Volume 20, Issue 1, pp58-66.

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