  Medical images are often characterized by their structured anatomical
representations and spatially inhomogeneous contrasts. Leveraging anatomical
priors in neural networks can greatly enhance their utility in
resource-constrained clinical settings. Prior research has harnessed such
information for image segmentation, yet progress in deformable image
registration has been modest. Our work introduces textSCF, a novel method that
integrates spatially covariant filters and textual anatomical prompts encoded
by visual-language models, to fill this gap. This approach optimizes an
implicit function that correlates text embeddings of anatomical regions to
filter weights, relaxing the typical translation-invariance constraint of
convolutional operations. TextSCF not only boosts computational efficiency but
can also retain or improve registration accuracy. By capturing the contextual
interplay between anatomical regions, it offers impressive inter-regional
transferability and the ability to preserve structural discontinuities during
registration. TextSCF's performance has been rigorously tested on inter-subject
brain MRI and abdominal CT registration tasks, outperforming existing
state-of-the-art models in the MICCAI Learn2Reg 2021 challenge and leading the
leaderboard. In abdominal registrations, textSCF's larger model variant
improved the Dice score by 11.3% over the second-best model, while its smaller
variant maintained similar accuracy but with an 89.13% reduction in network
parameters and a 98.34\% decrease in computational operations.
