SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images

Autor: Malte Hoffmann, Douglas N. Greve, Benjamin Billot, Juan Eugenio Iglesias, Adrian V. Dalca, Bruce Fischl
Rok vydání: 2022
Předmět:
FOS: Computer and information sciences
Optimization problem
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image registration
Neuroimaging
Geometric shape
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Image Processing
Computer-Assisted
FOS: Electrical engineering
electronic engineering
information engineering
Computer vision
Electrical and Electronic Engineering
Invariant (computer science)
Radiological and Ultrasound Technology
business.industry
Image and Video Processing (eess.IV)
Contrast (statistics)
Electrical Engineering and Systems Science - Image and Video Processing
Magnetic Resonance Imaging
Computer Science Applications
Range (mathematics)
Generative model
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
Neurons and Cognition (q-bio.NC)
Artificial intelligence
Focus (optics)
business
Software
Zdroj: IEEE Transactions on Medical Imaging. 41:543-558
ISSN: 1558-254X
0278-0062
DOI: 10.1109/tmi.2021.3116879
Popis: We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at https://w3id.org/synthmorph.
16 pages, 15 figures, 3 tables, deformable image registration, data independence, deep learning, MRI-contrast invariance, anatomy agnosticism, final published version
Databáze: OpenAIRE
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