Over the last decades, medical imaging techniques have
played a crucial role in healthcare, supporting radiologists
and facilitating patient diagnosis. With the advent of faster
and higher-quality imaging technologies, the amount of data
that is possible to collect for each patient is paving the way
toward personalised medicine. As a result, automating simple
image analysis operations, such as lesion localisation and
quantification, would greatly help clinicians focus energy
and attention on tasks best done by human intelligence.
Most recently, Artificial Intelligence (AI) research is accelerating
in healthcare, providing tools that often perform on par or
even better than humans in conceptually simple image processing
operations. In our work, we pay special attention to
the problem of automating semantic segmentation, where an
image is partitioned into multiple semantically meaningful
regions, separating the anatomical components of interest.
Unfortunately, developing effective AI segmentation tools usually
needs large quantities of annotated data. Conversely,
obtaining large-scale annotated datasets is difficult in medical
imaging, as it requires experts and is time-consuming.
For this reason, we develop automated methods to reduce the
need for collecting high-quality annotated data, both in terms
of the number and type of required annotations. We make
this possible by constraining the data representation learned
by our method to be semantic or by regularising the model
predictions to satisfy data-driven spatio-temporal priors. In
the thesis, we also open new avenues for future research using
AI with limited annotations, which we believe is key to
developing robust AI models for medical image analysis