1,209 research outputs found
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
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to
conventional registration methods. However, these methods often still cannot
achieve the same performance as conventional registration methods because they
are either limited to small deformation or they fail to handle a superposition
of large and small deformations without producing implausible deformation
fields with foldings inside.
In this paper, we identify important strategies of conventional registration
methods for lung registration and successfully developed the deep-learning
counterpart. We employ a Gaussian-pyramid-based multilevel framework that can
solve the image registration optimization in a coarse-to-fine fashion.
Furthermore, we prevent foldings of the deformation field and restrict the
determinant of the Jacobian to physiologically meaningful values by combining a
volume change penalty with a curvature regularizer in the loss function.
Keypoint correspondences are integrated to focus on the alignment of smaller
structures.
We perform an extensive evaluation to assess the accuracy, the robustness,
the plausibility of the estimated deformation fields, and the transferability
of our registration approach. We show that it achieves state-of-the-art results
on the COPDGene dataset compared to conventional registration method with much
shorter execution time. In our experiments on the DIRLab exhale to inhale lung
registration, we demonstrate substantial improvements (TRE below mm) over
other deep learning methods. Our algorithm is publicly available at
https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/
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