MIRNF: Medical Image Registration via Neural Fields

06/07/2022
by   Shanlin Sun, et al.
16

Image registration is widely used in medical image analysis to provide spatial correspondences between two images. Recently learning-based methods utilizing convolutional neural networks (CNNs) have been proposed for solving image registration problems. The learning-based methods tend to be much faster than traditional optimization-based methods, but the accuracy improvements gained from the complex CNN-based methods are modest. Here we introduce a new deep-neural net-based image registration framework, named MIRNF, which represents the correspondence mapping with a continuous function implemented via Neural Fields. MIRNF outputs either a deformation vector or velocity vector given a 3D coordinate as input. To ensure the mapping is diffeomorphic, the velocity vector output from MIRNF is integrated using the Neural ODE solver to derive the correspondences between two images. Furthermore, we propose a hybrid coordinate sampler along with a cascaded architecture to achieve the high-similarity mapping performance and low-distortion deformation fields. We conduct experiments on two 3D MR brain scan datasets, showing that our proposed framework provides state-of-art registration performance while maintaining comparable optimization time.

READ FULL TEXT

page 9

page 12

page 13

research
02/25/2022

Implicit Optimizer for Diffeomorphic Image Registration

Diffeomorphic image registration is the underlying technology in medical...
research
10/20/2021

A Learning Framework for Diffeomorphic Image Registration based on Quasi-conformal Geometry

Image registration, the process of defining meaningful correspondences b...
research
08/16/2019

A Cooperative Autoencoder for Population-BasedRegularization of CNN Image Registration

Spatial transformations are enablers in a variety of medical image analy...
research
08/16/2019

A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration

Spatial transformations are enablers in a variety of medical image analy...
research
11/10/2022

SETGen: Scalable and Efficient Template Generation Framework for Groupwise Medical Image Registration

Template generation is a crucial step of groupwise image registration wh...
research
03/05/2022

Coordinate Translator for Learning Deformable Medical Image Registration

The majority of deep learning (DL) based deformable image registration m...

Please sign up or login with your details

Forgot password? Click here to reset