UniReg introduces a conditional unified neural model for multi-scenario CT registration that conditions on anatomical priors, inter/intra-subject type, and instance features to achieve higher accuracy and cross-scenario generalization than task-specific networks.
Efficient inference in fully connected crfs with gaussian edge potentials
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UniReg: A Universal Model for Controllable CT Image Registration
UniReg introduces a conditional unified neural model for multi-scenario CT registration that conditions on anatomical priors, inter/intra-subject type, and instance features to achieve higher accuracy and cross-scenario generalization than task-specific networks.