We introduce a learning-based strategy for multi-modal registration of images acquired with any modality, without requiring real dataduring training . While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on . In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training. We demonstrate that this strategy enables robust registration of arbitrary modalities, without the need to retrain for a new modality. Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data . We present extensive experiments demonstrating that this strategy enables registration of modalities not seen during training and surpasses the state of art in cross-contrast registration . Our code is integrated with the VoxelMorph library at: URL
We introduce a learning strategy for contrast-invariant image registration without requiring imaging data . While classical registration methods accurately estimate the spatial correspondence between images , they solve a costly optimization problem for every image pair . Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model for diverse label maps and images that exposes networks to a wide range of variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary image contrasts without the need to retrain for new modalities. We demonstrate registration accuracy that most often surpasses the state of the art both within and across modalities, using a single model. Critically, we show that input labels from which we synthesize images need not be of actual anatomy: training on randomly generated geometric shapes also results in competitive registration performance , albeit slightly less accurate, while alleviating the dependency on real data of any kind . Our code is available at: URL
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learning-based strategy for multi-modal registration of images acquired with any modality, without requiring real dataduring training
learning strategy for contrast-invariant image registration without requiring imaging data
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can accurately align multi-modal image pairs
accurately estimate the spatial correspondence between images
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new pairof images
image pair
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of the specific anatomy and modalities they were trained on . In contrast, our
with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our
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to synthesize
for diverse
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gray-scale images that expose a network
images that exposes networks
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anatomy and contrast during training. We demonstrate
variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing
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modalities,
image contrasts
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a new modality.
new modalities. We demonstrate registration accuracy that most often surpasses the state of the art both within and across modalities, using a single model.
A
from which we synthesize images
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synthesized shapes , or supervoxels,
generated geometric shapes also
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and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data . We present extensive experiments demonstrating that this strategy enables registration of modalities not seen during training and surpasses the state of art in cross-contrast registration
, albeit slightly less accurate, while alleviating the dependency on real data of any kind
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integrated with the VoxelMorph library
available