Primary supervisorYasmeen George
The number of kidney cancer patients is increasing each year. Computed Tomography (CT) scans of the kidneys are useful to assess tumors and study tumor morphology. Semantic segmentation techniques enable the identification of kidney and surrounding anatomy on the pixel level. This allows clinicians to provide accurate treatment plans and improve efficiency. The large size of CT volumes poses challenges for deep segmentation methods as it cannot be accommodated on a single GPU in its original resolution. Downsampling CT scans influences the segmentation performance. In this project, 3D and 2D U-Net architectures will be used for semantic segmentation of kidney CT volumes into three classes kidney, tumor, and cyst. The trained models will also be evaluated on another modality (potentially MRI).
Automated medical image segmentation of kidney CT volumes into three classes kidney, tumor, and cyst
Deep learning, medical imaging (CT and MRI), image segmentation, 2D/3D image processing