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unified artificial intelligence (AI) model capable of segmenting 3D medical images from standard clinical scans and generating 3D meshes across multiple imaging modalities. The project will also investigate
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focus on the medical image processing aspect of the Birth4Cast simulator by researching and developing automated image segmentation procedures to extract the pelvic floor muscle complex and the fetal head
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practical solutions for safer participation. Projects may be supervised or co-supervised by experts from Engineering Science and the Medical Sciences Division. Potential Supervisors: Prof. Thomas Okell, Prof
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students. Option 2 - Ki67 reloaded: Investigating novel links between the mitotic chromosome periphery, cell division fidelity and breast cancer The mitotic chromosome periphery (MCP) is an enigmatic sheath
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THR demand in younger patients expected to increase fivefold by 2030, revision surgeries will also rise. To improve implant positioning, image-guided navigation is increasingly used in complex THR
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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recognition from aerial imagery in tropical forests. The research will investigate the long-tailed open-ended semantic segmentation problem and advance new approaches for uncertainty estimation and confidence
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at higher risk offered PSA blood tests which are not definitive. Our research aims to develop an image-based approach to screening, combining PSA testing with MRI to better identify aggressive cancers