8,464 research outputs found
Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
Deformable image registration is a fundamental task in medical image analysis
and plays a crucial role in a wide range of clinical applications. Recently,
deep learning-based approaches have been widely studied for deformable medical
image registration and achieved promising results. However, existing deep
learning image registration techniques do not theoretically guarantee
topology-preserving transformations. This is a key property to preserve
anatomical structures and achieve plausible transformations that can be used in
real clinical settings. We propose a novel framework for deformable image
registration. Firstly, we introduce a novel regulariser based on
conformal-invariant properties in a nonlinear elasticity setting. Our
regulariser enforces the deformation field to be smooth, invertible and
orientation-preserving. More importantly, we strictly guarantee topology
preservation yielding to a clinical meaningful registration. Secondly, we boost
the performance of our regulariser through coordinate MLPs, where one can view
the to-be-registered images as continuously differentiable entities. We
demonstrate, through numerical and visual experiments, that our framework is
able to outperform current techniques for image registration.Comment: 11 pages, 3 figure
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Pathologists need to combine information from differently stained pathology
slices for accurate diagnosis. Deformable image registration is a necessary
technique for fusing multi-modal pathology slices. This paper proposes a hybrid
deep feature-based deformable image registration framework for stained
pathology samples. We first extract dense feature points via the detector-based
and detector-free deep learning feature networks and perform points matching.
Then, to further reduce false matches, an outlier detection method combining
the isolation forest statistical model and the local affine correction model is
proposed. Finally, the interpolation method generates the deformable vector
field for pathology image registration based on the above matching points. We
evaluate our method on the dataset of the Non-rigid Histology Image
Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019
conference. Our technique outperforms the traditional approaches by 17% with
the Average-Average registration target error (rTRE) reaching 0.0034. The
proposed method achieved state-of-the-art performance and ranked 1st in
evaluating the test dataset. The proposed hybrid deep feature-based
registration method can potentially become a reliable method for pathology
image registration.Comment: 22 pages, 12 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
Medical image registration is one of the key processing steps for biomedical
image analysis such as cancer diagnosis. Recently, deep learning based
supervised and unsupervised image registration methods have been extensively
studied due to its excellent performance in spite of ultra-fast computational
time compared to the classical approaches. In this paper, we present a novel
unsupervised medical image registration method that trains deep neural network
for deformable registration of 3D volumes using a cycle-consistency. Thanks to
the cycle consistency, the proposed deep neural networks can take diverse pair
of image data with severe deformation for accurate registration. Experimental
results using multiphase liver CT images demonstrate that our method provides
very precise 3D image registration within a few seconds, resulting in more
accurate cancer size estimation.Comment: accepted for MICCAI 201
Attention for Image Registration (AiR): an unsupervised Transformer approach
Image registration as an important basis in signal processing task often encounter the problem of stability and efficiency. Non-learning registration approaches rely on the optimization of the similarity metrics between the fix- and moving images. Yet, those approaches are usually costly in both time and space complexity. The problem can be worse when the size of the image is large or the deformations between the images are severe. Recently, deep learning, or precisely saying, the convolutional neural network (CNN) based image registration methods have been widely investigated in the research community and show promising effectiveness to overcome the weakness of non-learning based methods. To explore the advanced learning approaches in image registration problem for solving practical issues, we present in this paper a method of introducing attention mechanism in deformable image registration problem. The proposed approach is based on learning the deformation field with a Transformer framework that does not rely on the CNN but can be efficiently trained on GPGPU devices also. Our method learns an artificially generated deformation map and be tested on a MINST dataset
Learning the Effect of Registration Hyperparameters with HyperMorph
We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) at https://www.melba-journal.or
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide
variety of approaches have been proposed. However, only a few studies have
comprehensively compared medical image registration approaches on a wide range
of clinically relevant tasks. This limits the development of registration
methods, the adoption of research advances into practice, and a fair benchmark
across competing approaches. The Learn2Reg challenge addresses these
limitations by providing a multi-task medical image registration data set for
comprehensive characterisation of deformable registration algorithms. A
continuous evaluation will be possible at
https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of
anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR),
availability of annotations, as well as intra- and inter-patient registration
evaluation. We established an easily accessible framework for training and
validation of 3D registration methods, which enabled the compilation of results
of over 65 individual method submissions from more than 20 unique teams. We
used a complementary set of metrics, including robustness, accuracy,
plausibility, and runtime, enabling unique insight into the current
state-of-the-art of medical image registration. This paper describes datasets,
tasks, evaluation methods and results of the challenge, as well as results of
further analysis of transferability to new datasets, the importance of label
supervision, and resulting bias. While no single approach worked best across
all tasks, many methodological aspects could be identified that push the
performance of medical image registration to new state-of-the-art performance.
Furthermore, we demystified the common belief that conventional registration
methods have to be much slower than deep-learning-based methods
- …