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Development of new computer vision techniques for automated reconstruction of curvilinear structures from images

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Imaging techniques play an ever-increasing role in our daily lives. In particular, networks of curvilinear structures (i.e., muscle fibres, nerves, blood vessels) are pervasive in images produced both in laboratory and in clinical practice. They appear at different scales, colours, and orientations. The analysis of these complex structures is of great importance to the understanding of disease mechanism, diagnosis, treatment planning and monitoring of therapeutic effects. This project aims to develop new techniques for automated detection and reconstruction of complex curvilinear structures in biomedical images by embracing newly emerged deep learning techniques and graph theory.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives. The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. We offer a supportive working environment with flexible family support for all our staff and students and applications for part-time study are encouraged. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.

Informal enquiries regarding this project should be made to Dr Yalin Zheng (). All general enquiries should be directed to Mrs Sue Jones ().

To apply please send your CV and a covering letter to Dr Zheng () with a copy to

Funding Notes

The successful applicant will be expected to provide the funding for tuition fees, bench fees of approximately £3,000 per year and all living expenses. Details of the cost of study can be found on the University website. There is NO funding attached to this project.

We have a thriving international researcher community and encourage applications from students of any nationality able to fund their own studies (Government scholarship), or who wish to apply for their own funding (e.g. China Scholarship Council).

References

1. Hu Q, Abràmoff MD, Garvin MK. J Med Imaging. 2015;2(4):044001-1-16.
2. Zhao Y, Rada L, Chen K, Harding S, Zheng Y. IEEE Trans Med Img. 2015;34:1797-807.
3. LeCun Y, Bengio Y, Hinton G. Nature. 2015;521:436-444.

How good is research at University of Liverpool in Clinical Medicine?
(joint submission with Liverpool School of Tropical Medicine)

FTE Category A staff submitted: 143.50

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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