Medical image segmentation is a relevant task as it serves as the first step
for several diagnosis processes, thus it is indispensable in clinical usage.
Whilst major success has been reported using supervised techniques, they assume
a large and well-representative labelled set. This is a strong assumption in
the medical domain where annotations are expensive, time-consuming, and
inherent to human bias. To address this problem, unsupervised techniques have
been proposed in the literature yet it is still an open problem due to the
difficulty of learning any transformation pattern. In this work, we present a
novel optimisation model framed into a new CNN-based contrastive registration
architecture for unsupervised medical image segmentation. The core of our
approach is to exploit image-level registration and feature-level from a
contrastive learning mechanism, to perform registration-based segmentation.
Firstly, we propose an architecture to capture the image-to-image
transformation pattern via registration for unsupervised medical image
segmentation. Secondly, we embed a contrastive learning mechanism into the
registration architecture to enhance the discriminating capacity of the network
in the feature-level. We show that our proposed technique mitigates the major
drawbacks of existing unsupervised techniques. We demonstrate, through
numerical and visual experiments, that our technique substantially outperforms
the current state-of-the-art unsupervised segmentation methods on two major
medical image datasets.Comment: 11 pages, 3 figure