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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

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.

Details

Date:
April 26
Time:
2:30 pm - 3:30 pm
Event Category:

Organizer

Center for Research in Computer Vision

Venue

HEC 101
4328 Scorpius Street, HEC 245F
Orlando, FL 32816 United States
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Phone:
4078231119