2018 JRNL PP Ozan Oktay - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Incorporation of prior knowledge about organ
shape and location is key to improve performance of image
analysis approaches. In particular, priors can be useful in
caseswhere images are corrupted and contain artefacts due
to limitations in image acquisition. The highly constrained
nature of anatomical objects can be well captured with
learning-based techniques. However, in most recent and
promising techniques such as CNN-based segmentation
it is not obvious how to incorporate such prior knowledge.
State-of-the-art methods operate as pixel-wise classifiers
where the training objectives do not incorporate the
structure and inter-dependencies of the output. To overcome
this limitation, we propose a generic training strategy
that incorporates anatomical prior knowledge into CNNs
through a new regularisation model, which is trained endto-
end. The new framework encourages models to follow
the global anatomical properties of the underlying anatomy
(e.g. shape, label structure) via learnt non-linear representations
of the shape. We show that the proposed approach
can be easily adapted to different analysis tasks (e.g. image
enhancement, segmentation) and improve the prediction
accuracy of the state-of-the-art models. The applicability of
our approachis shown onmulti-modal cardiac data sets and
public benchmarks. In addition, we demonstrate how the
learnt deep models of 3-D shapes can be interpreted and
used as biomarkers for classificationof cardiac pathologies.