Medical Image Analysis using Deep Learning at Kingston University on FindAPhD.com

Prof D Makris Applications accepted all year round Self-Funded PhD Students Only

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London United Kingdom Applied Mathematics Data Analysis Electrical Engineering Computer Science Information Services Mathematics Radiology Statistics

About the Project

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, and preferably MSc) 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 (d.makris@kingston.ac.uk) 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

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