Friday, April 26, 2019 2:30 p.m. to 3:30 p.m.

Speaker: Mert R. Sabuncu

From: Cornell University

 Abstract

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.

For more info, please follow this link.

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