Automated medical image analysis is a growing research field with various applications in
modern healthcare. Furthermore, a multitude of imaging techniques (or modalities) have been
developed, such as Magnetic Resonance (MR) and Computed Tomography (CT), to attenuate
different organ characteristics. Research on image analysis is predominately driven by deep
learning methods due to their demonstrated performance. In this thesis, we argue that their success and generalisation relies on learning good latent representations. We propose methods for
learning spatial representations that are suitable for medical image data, and can combine information coming from different modalities. Specifically, we aim to improve cardiac MR segmentation, a challenging task due to varied images and limited expert annotations, by considering
complementary information present in (potentially unaligned) images of other modalities.
In order to evaluate the benefit of multimodal learning, we initially consider a synthesis task
on spatially aligned multimodal brain MR images. We propose a deep network of multiple
encoders and decoders, which we demonstrate outperforms existing approaches. The encoders
(one per input modality) map the multimodal images into modality invariant spatial feature
maps. Common and unique information is combined into a fused representation, that is robust
to missing modalities, and can be decoded into synthetic images of the target modalities. Different experimental settings demonstrate the benefit of multimodal over unimodal synthesis,
although input and output image pairs are required for training. The need for paired images can
be overcome with the cycle consistency principle, which we use in conjunction with adversarial
training to transform images from one modality (e.g. MR) to images in another (e.g. CT). This
is useful especially in cardiac datasets, where different spatial and temporal resolutions make
image pairing difficult, if not impossible.
Segmentation can also be considered as a form of image synthesis, if one modality consists of
semantic maps. We consider the task of extracting segmentation masks for cardiac MR images,
and aim to overcome the challenge of limited annotations, by taking into account unannanotated images which are commonly ignored. We achieve this by defining suitable latent spaces,
which represent the underlying anatomies (spatial latent variable), as well as the imaging characteristics (non-spatial latent variable). Anatomical information is required for tasks such as
segmentation and regression, whereas imaging information can capture variability in intensity
characteristics for example due to different scanners. We propose two models that disentangle
cardiac images at different levels: the first extracts the myocardium from the surrounding information, whereas the second fully separates the anatomical from the imaging characteristics.
Experimental analysis confirms the utility of disentangled representations in semi-supervised
segmentation, and in regression of cardiac indices, while maintaining robustness to intensity
variations such as the ones induced by different modalities.
Finally, our prior research is aggregated into one framework that encodes multimodal images
into disentangled anatomical and imaging factors. Several challenges of multimodal cardiac
imaging, such as input misalignments and the lack of expert annotations, are successfully handled in the shared anatomy space. Furthermore, we demonstrate that this approach can be used
to combine complementary anatomical information for the purpose of multimodal segmentation. This can be achieved even when no annotations are provided for one of the modalities.
This thesis creates new avenues for further research in the area of multimodal and disentangled learning with spatial representations, which we believe are key to more generalised deep
learning solutions in healthcare
