65 research outputs found
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
Neural networks have been proposed for medical image registration by
learning, with a substantial amount of training data, the optimal
transformations between image pairs. These trained networks can further be
optimized on a single pair of test images - known as test-time optimization.
This work formulates image registration as a meta-learning algorithm. Such
networks can be trained by aligning the training image pairs while
simultaneously improving test-time optimization efficacy; tasks which were
previously considered two independent training and optimization processes. The
proposed meta-registration is hypothesized to maximize the efficiency and
effectiveness of the test-time optimization in the "outer" meta-optimization of
the networks. For image guidance applications that often are time-critical yet
limited in training data, the potentially gained speed and accuracy are
compared with classical registration algorithms, registration networks without
meta-learning, and single-pair optimization without test-time optimization
data. Experiments are presented in this paper using clinical transrectal
ultrasound image data from 108 prostate cancer patients. These experiments
demonstrate the effectiveness of a meta-registration protocol, which yields
significantly improved performance relative to existing learning-based methods.
Furthermore, the meta-registration achieves comparable results to classical
iterative methods in a fraction of the time, owing to its rapid test-time
optimization process.Comment: Accepted to ASMUS 2022 Workshop at MICCA
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