Over the last decade, research in medical imaging has made significant
progress in addressing challenging tasks such as image registration and image
segmentation. In particular, the use of model-based approaches has been key
in numerous, successful advances in methodology. The advantage of modelbased
approaches is that they allow the incorporation of prior knowledge
acting as a regularisation that favours plausible solutions over implausible
ones. More recently, medical imaging has moved away from hand-crafted, and
often explicitly designed models towards data-driven, implicit models that
are constructed using machine learning techniques. This has led to major
improvements in all stages of the medical imaging pipeline, from acquisition
and reconstruction to analysis and interpretation. As more and more imaging
data is becoming available, e.g., from large population studies, this trend is
likely to continue and accelerate. At the same time new developments in
machine learning, e.g., deep learning, as well as significant improvements
in computing power, e.g., parallelisation on graphics hardware, offer new
potential for data-driven, semantic and intelligent medical imaging. This
article outlines the work of the BioMedIA group in this area and highlights
some of the challenges and opportunities for future work.