  Classical pairwise image registration methods search for a spatial
transformation that optimises a numerical measure that indicates how well a
pair of moving and fixed images are aligned. Current learning-based
registration methods have adopted the same paradigm and typically predict, for
any new input image pair, dense correspondences in the form of a dense
displacement field or parameters of a spatial transformation model. However, in
many applications of registration, the spatial transformation itself is only
required to propagate points or regions of interest (ROIs). In such cases,
detailed pixel- or voxel-level correspondence within or outside of these ROIs
often have little clinical value. In this paper, we propose an alternative
paradigm in which the location of corresponding image-specific ROIs, defined in
one image, within another image is learnt. This results in replacing image
registration by a conditional segmentation algorithm, which can build on
typical image segmentation networks and their widely-adopted training
strategies. Using the registration of 3D MRI and ultrasound images of the
prostate as an example to demonstrate this new approach, we report a median
target registration error (TRE) of 2.1 mm between the ground-truth ROIs defined
on intraoperative ultrasound images and those propagated from the preoperative
MR images. Significantly lower (>34%) TREs were obtained using the proposed
conditional segmentation compared with those obtained from a
previously-proposed spatial-transformation-predicting registration network
trained with the same multiple ROI labels for individual image pairs. We
conclude this work by using a quantitative bias-variance analysis to provide
one explanation of the observed improvement in registration accuracy.
