  Medical image registration and segmentation are critical tasks for several
clinical procedures. Manual realisation of those tasks is time-consuming and
the quality is highly dependent on the level of expertise of the physician. To
mitigate that laborious task, automatic tools have been developed where the
majority of solutions are supervised techniques. However, in medical domain,
the strong assumption of having a well-representative ground truth is far from
being realistic. To overcome this challenge, unsupervised techniques have been
investigated. However, they are still limited in performance and they fail to
produce plausible results. In this work, we propose a novel unified
unsupervised framework for image registration and segmentation that we called
PC-SwinMorph. The core of our framework is two patch-based strategies, where we
demonstrate that patch representation is key for performance gain. We first
introduce a patch-based contrastive strategy that enforces locality conditions
and richer feature representation. Secondly, we utilise a 3D
window/shifted-window multi-head self-attention module as a patch stitching
strategy to eliminate artifacts from the patch splitting. We demonstrate,
through a set of numerical and visual results, that our technique outperforms
current state-of-the-art unsupervised techniques.
