Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.

ABSTRACT: Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registratio

SUBMITTER: Hoffmann M 

PROVIDER: S-EPMC11247402 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature