Object Detecting and Tracking in Medical X-ray Images

Eligibility: UK/EU/International graduates with the required entry requirements

Duration: Full-time – between three and three and a half years fixed term

Application deadline: This opportunity will remain open until a suitable candidate is identified. Early application is therefore recommended

Interview dates: Will be confirmed to shortlisted candidates

Start date: PhDs can start in either September, January or May

For queries contact Dr. YingLiang Ma

Introduction

The Research Centre for Computational Science and Mathematical Modelling (CSM) is based in the Faculty of Engineering, Environment and Computing (EEC) of Coventry University.  It provides a hub to develop cutting edge research in the areas of Artificial Intelligence, Data Science and Future Computing Technologies. The centre has a vocation to push the boundaries of both fundamental science and practical applications. 

About the project

Real-time detecting and tracking surgical instruments in X-ray images is an important task for guiding minimally invasive cardiac surgeries. As blood vessels and heart tissues are hardly visible under X-ray, to overcome this problem, 3D roadmaps can be overlaid onto X-ray images to add anatomical information. 3D roadmaps are the anatomical models which can be generated from computed tomography (CT) or images magnetic resonance (MR) images.  However, the accuracy of 3D roadmap guiding systems relies on two issues: 1) respiratory and cardiac motion compensation, and 2) registration between the 2D X-ray images and the 3D roadmap. Detecting and tracking surgical instruments, such as catheters and guidewires, provides more information to help increasing the accuracy and dealing with both issues.

The standard machine learning algorithms and image processing methods need to be adapted and re-designed to effectively localize surgical instruments in X-ray images, as medical X-ray images are very different compared to natural images. The proposer has developed a new concept of embedding machine learning methods into image filter algorithms, which could dramatically improve the accuracy and efficiency of real-time detection and tracking methods for surgical instruments.

Funding 

This project is offered on a self-funded or externally funded basis. We particular welcome applications from candidates with funding from their home governments or sponsorship from companies.

Postgraduate Loans ( Masters  by Research and Doctoral )

You may be able to get a UK loan from Student Finance England or Student Finance Wales for a postgraduate degree in any subject - Funding for postgraduate study.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills.  All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College which provides support with career development activities.

The candidate for this project will work in the Centre for Computational Science and Mathematical Modelling amongst other researchers employing similar data science techniques to a wide variety of problems and applications.  With access to cutting edge techniques and high performance computing resources, the Centre for Computational Science and Mathematical Modelling is an ideal place to pursue a PhD in Data Science, Machine Learning, or Artificial Intelligence.

Entry requirements

• A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 
  PLUS 
  the potential to engage in innovative research and to complete the PhD within a 3.5 years
• a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

Additionally

The ideal candidate should have experience in programming languages such as C/C++.  Experience with developing image-processing tools using the OpenCV environment or similar open-source software is a plus.

How to apply

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.