Spatially Covariant Image Registration with Text Prompts
This addresses deformable image registration for medical imaging, offering computational efficiency and accuracy gains in clinical settings, though it appears incremental by building on prior anatomical priors and visual-language models.
The paper tackles medical image registration by introducing textSCF, a method that integrates spatially covariant filters with textual anatomical prompts from visual-language models, relaxing translation-invariance constraints. It outperforms state-of-the-art models in brain MRI and abdominal CT tasks, with a larger variant improving Dice scores by 11.3% and a smaller variant reducing parameters by 89.13% and computational operations by 98.34% while maintaining accuracy.
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.