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Medical Image Analysis using Deep Learning

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

Project Description

Medical Image Analysis aims to extract information from available visual modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultasonography (US) to detect conspicuous structures, quantigy their properties, evaluate the effectiveness of treatment or diagnose a condition.

This project will focus on Computer Vision and Machine/Deep Learning approaches, aiming to develop tools that automate the process of Medical Image Analysis and support clinicians in their task of making appropriate decisions.

Candidates should have appropriate academic qualifications (first or upper second class honours or MSc degree) in Computer Science, Engineering, Mathematics, Physics or other relevant area, strong background in programming and desire to become experts in Computer Vision and Machine Deep Learning.

Qualified applicants are encouraged to contact Prof Dimitrios Makris () to informally discuss the project.

Supervisor’s profile:
https://www.kingston.ac.uk/staff/profile/professor-dimitrios-makris-151/

Google Scholar profile:
https://scholar.google.co.uk/citations?user=vHv7JRcAAAAJ


Funding Notes

No funding is available for this project

References

[1] Bakas, Spyridon, Doulgerakis-Kontoudis, Matthaios, Hunter, Gordon, Sidhu, Paul S., Makris, Dimitrios and Chatzimichail, Katerina (2019) Evaluation of indirect methods for motion compensation in 2D focal liver lesion Contrast-Enhanced Ultrasound (CEUS) imaging. Ultrasound in Medicine and Biology, 45(6), pp. 1380-1396. ISSN (print) 0301-5629

[2] Bakas, Spyridon, Makris, Dimitrios, Hunter, Gordon J.A., Fang, Cheng, Sidhu, Paul S. and Chatzimichail, Katerina (2017) Automatic identification of the optimal reference frame for segmentation and quantification of focal liver lesions in contrast-enhanced ultrasound. Ultrasound in Medicine & Biology, 43(10), pp. 2438-2451. ISSN (print) 0301-5629


[3] Bakas, S., Chatzimichail, K, Hunter, G. J. A., Labbe, B., Sidhu, P and Makris, D. (2017) Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 5(5), pp. 329-338. ISSN (print) 2168-1163.

[4] Bakas, S., Makris, D., Sidhu, P.S. and Chatzimichail, K. (2014) Automatic Identification and Localisation of Potential Malignancies in Contrast-Enhanced Ultrasound Liver Scans Using Spatio-Temporal Features. In: Sixth International Workshop on Abdominal Imaging: Computational and Clinical Applications; 14 Sep 2014, Boston, U.S.A.. (Lecture Notes in Computer Science, no. 8676) ISBN 9783319136912


How good is research at Kingston University in Computer Science and Informatics?

FTE Category A staff submitted: 10.20

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

Click here to see the results for all UK universities

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