Abstract
Image analysis for cancer imaging is becoming increasingly integrated into clinical workflow. As imaging technology is becoming more and more sophisticated, providing volumetric, multi-modality and dynamic acquisitions, the large amount of spatiotemporal data available poses increasingly challenging problems for the radiologists, oncologists, and other clinicians involved in cancer treatment, calling for automated, robust and accurate image analysis solutions. One aspect of interpreting such data correctly is the problem of patient motion, due to different scanning systems, patient movements, or respiratory motion. Over the past five years, the Biomedical Image Analysis lab at Oxford has developed a range of image analysis tools for multi-modal and dynamic image motion correction, in particular for lung cancer and colorectal cancer. A summary of these efforts is given here, and future research challenges are identified.
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