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VoxelMorph: Unsupervised learning for fast probabilistic diffeomorphic registration
April 26 @ 2:30 pm - 3:30 pm
Speaker: Mert R. Sabuncu
From: Cornell University
In this talk, I will present VoxelMorph – a fast learning-based framework for deformable, pairwise image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets and/or with rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. I will present some empirical results that demonstrate the capabilities of our approach and compare performance with state-of-the-art methods. Our code is freely available at https://github.com/voxelmorph/voxelmorph.
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