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(MRC DTP) Development of a software system to automatically detect and quantify foot collapse

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  • Full or part time
    Dr Claudia Lindner
    Prof T Cootes
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Charcot arthropathy is collapse of a joint secondary to peripheral neuropathy, most commonly secondary to diabetes or alcoholism. It is a debilitating condition which causes progressive destruction of the joint with associated pain and alteration in the mechanics of walking. The foot often deforms, increasing the risk of joint instability, soft tissue ulceration, and ultimately lower extremity amputation. Prompt diagnosis allows patients to have appropriate podiatry assessment, orthotics, conservative bracing or surgical fixation. Current diagnosis and analysis of severity relies on radiologically derived geometric measurements on a lateral foot radiograph. These measurements are time consuming to obtain and are susceptible to inter- and intra-observer variability.
This project will develop and validate a software system to automatically detect and quantify foot collapse. Machine-learning methods will be applied to automatically and reliably identify feature points on foot radiographs (see Figure 1). The automatically identified feature points will then be used to automatically calculate geometric measurements of relevance to detecting and measuring foot collapse (e.g. Meary’s angle). The performance of the system in obtaining the latter will be compared to manual ground truth measurements. Furthermore, Statistical Shape Models will be used to study the bone shape of the foot and ankle in diseased cases, aiming to improve methods for early detection and assessment of progression. The methods and software to be developed has the potential to save on reporting time for radiologists and to increase the reproducibility of disease-related geometric measurements. Furthermore, earlier detection of disease is likely to improve patient outcome and reduce costs to the NHS.
The student will join a well-established research group, and will gain extensive experience of working in an interdisciplinary team. They will gain vast knowledge of state-of-the-art machine vision algorithm development for clinical imaging problems. The student will be placed in the translational space, and will have the opportunity to learn more about the pathway of progressing research towards impact in clinical practice. Finally, they will work with our clinical partners from the Salford Royal NHS Foundation Trust and gain in-depth knowledge of foot and ankle anatomy and pathology, particularly the clinical imaging, diagnosis and treatment of Charcot arthropathy.

https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/
https://personalpages.manchester.ac.uk/staff/claudia.lindner/

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website www.manchester.ac.uk/mrcdtpstudentships

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

C. Lindner, P.A. Bromiley, M.C. Ionita and T.F. Cootes. Robust and Accurate Shape Model Matching using Random Forest Regression Voting. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(9) p. 1862-1874, 2015.
C. Lindner, S. Thiagarajah, J.M. Wilkinson, arcOGEN Consortium, G.A. Wallis and T.F. Cootes. Fully Automatic Segmentation of the Proximal Femur using Random Forest Regression Voting. IEEE Transactions on Medical Imaging, Vol. 32, No. 8, pages 1462-1472, 2013.
T.F. Cootes et al., "Active Shape Models - Their Training and Application." CVIU. 61, 38-59,1995.



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