2,601 research outputs found
Recursive Cascaded Networks for Unsupervised Medical Image Registration
We present recursive cascaded networks, a general architecture that enables
learning deep cascades, for deformable image registration. The proposed
architecture is simple in design and can be built on any base network. The
moving image is warped successively by each cascade and finally aligned to the
fixed image; this procedure is recursive in a way that every cascade learns to
perform a progressive deformation for the current warped image. The entire
system is end-to-end and jointly trained in an unsupervised manner. In
addition, enabled by the recursive architecture, one cascade can be iteratively
applied for multiple times during testing, which approaches a better fit
between each of the image pairs. We evaluate our method on 3D medical images,
where deformable registration is most commonly applied. We demonstrate that
recursive cascaded networks achieve consistent, significant gains and
outperform state-of-the-art methods. The performance reveals an increasing
trend as long as more cascades are trained, while the limit is not observed.
Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
Image registration, the process of aligning two or more images, is the core
technique of many (semi-)automatic medical image analysis tasks. Recent studies
have shown that deep learning methods, notably convolutional neural networks
(ConvNets), can be used for image registration. Thus far training of ConvNets
for registration was supervised using predefined example registrations.
However, obtaining example registrations is not trivial. To circumvent the need
for predefined examples, and thereby to increase convenience of training
ConvNets for image registration, we propose the Deep Learning Image
Registration (DLIR) framework for \textit{unsupervised} affine and deformable
image registration. In the DLIR framework ConvNets are trained for image
registration by exploiting image similarity analogous to conventional
intensity-based image registration. After a ConvNet has been trained with the
DLIR framework, it can be used to register pairs of unseen images in one shot.
We propose flexible ConvNets designs for affine image registration and for
deformable image registration. By stacking multiple of these ConvNets into a
larger architecture, we are able to perform coarse-to-fine image registration.
We show for registration of cardiac cine MRI and registration of chest CT that
performance of the DLIR framework is comparable to conventional image
registration while being several orders of magnitude faster.Comment: Accepted: Medical Image Analysis - Elsevie
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