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F-AHG-3: Medical image analysis: landmarking the human femur

This project is motivated by hip fracture, a hugely important health and wealth issue. Hip fracture amongst the elderly causes significant physical and emotional distress, and the cost to health services is considerable, with annual cases forecast to rise to 6.3 million worldwide by 2050. High risk individuals could receive targeted therapy (drugs, exercise), but current screening methods fail to spot the majority of individuals who go on to fracture.

Our recent research demonstrates the potential of cortical thickness maps. These are coloured renderings of the bone surface that show the thickness of the stiff, outer shell (cortex) which is responsible for most of the bone's strength. However, in order to answer the important questions (what type of cortical thickness distribution might predispose an individual to fracture, does any particular therapy increase cortical thickness in the right regions?) it is necessary to compare cortical thickness maps across many individuals.

This project will focus on the task of identifying corresponding landmarks on individual femurs, so they can be brought into alignment for cohort studies. The landmarks need to be identified automatically, or at least semi-automatically, in clinical CT data. The project would suit a student who has taken Module 3G4 and Project GG2. It will involve programming in C++, and the opportunity to learn more about computational geometry and graphics.

Our recent research has shown how to extract cortical thickness maps (top right, pink is thin, blue is thick) from CT data (top left) by deconvolution (bottom). The result is a detailed, accurate map of an individual bone.
For cohort studies, we need to bring multiple femurs into alignment, before comparing the aligned cortical thickness maps. The alignment process is ill-posed but can be better constrained by identifying specific, matching landmarks on the two surfaces.
This figure shows some of the classical anatomical landmarks on the proximal femur. But how can they be segmented (semi-)automatically from clinical CT data? [Image reproduced, with permission, from Radiopaedia.]
© Cambridge University Engineering Department
Information provided by Andrew Gee
Last updated: March 2017