  Deep learning models are sensitive to domain shift phenomena. A model trained
on images from one domain cannot generalise well when tested on images from a
different domain, despite capturing similar anatomical structures. It is mainly
because the data distribution between the two domains is different. Moreover,
creating annotation for every new modality is a tedious and time-consuming
task, which also suffers from high inter- and intra- observer variability.
Unsupervised domain adaptation (UDA) methods intend to reduce the gap between
source and target domains by leveraging source domain labelled data to generate
labels for the target domain. However, current state-of-the-art (SOTA) UDA
methods demonstrate degraded performance when there is insufficient data in
source and target domains. In this paper, we present a novel UDA method for
multi-modal cardiac image segmentation. The proposed method is based on
adversarial learning and adapts network features between source and target
domain in different spaces. The paper introduces an end-to-end framework that
integrates: a) entropy minimisation, b) output feature space alignment and c) a
novel point-cloud shape adaptation based on the latent features learned by the
segmentation model. We validated our method on two cardiac datasets by adapting
from the annotated source domain, bSSFP-MRI (balanced Steady-State Free
Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium
enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT
(target) for the cross-modality dataset. The results highlighted that by
enforcing adversarial learning in different parts of the network, the proposed
method delivered promising performance, compared to other SOTA methods.

Computer Vision and Pattern Recognition