Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification

Wang, Jian; Wells, William M.; Golland, Polina; Zhang, Miaomiao

doi:10.1007/978-3-030-00928-1_99

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…In addition to experimenting with different model settings, there are a number of other areas that could lead to potential improvement. As suggested in Sabuncu et al ( 2010 ), the simple data augmentation approach could probably be improved by augmenting based on the uncertainty of the image registration (Simpson et al, 2012 ; Iglesias et al, 2013 ; Wang et al, 2018 ), which would effectively “integrate out” this source of uncertainty. Such an approach would also need to consider the expected uncertainty with which target images could be aligned.…”

Section: Discussionmentioning

confidence: 99%

Factorisation-Based Image Labelling

Balbastre

Brudfors

et al. 2022

Front. Neurosci.

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Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

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Section: Discussionmentioning

confidence: 99%

Factorisation-Based Image Labelling

Balbastre

Brudfors

et al. 2022

Front. Neurosci.

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show abstract

“…In addition to experimenting with different model settings, there are a number of other areas that could lead to potential improvement. As suggested in Sabuncu et al [2010], the simple data augmentation approach could probably be improved by augmenting based on the uncertainty of the image registration [Simpson et al, 2012, Iglesias et al, 2013, Wang et al, 2018, which would effectively "integrate out" this source of uncertainty. Such an approach would also need to consider the expected uncertainty with which target images could be aligned.…”

Section: Discussionmentioning

confidence: 99%

Factorisation-based Image Labelling

Yu¹,

Balbastre²,

Brudfors³

et al. 2021

Preprint

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Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based label propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

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“…where σ 2 is the noise variance and M is the number of image voxels. The deformation φ −1 (Wang et al, 2018), we define a prior on the initial velocity field ṽ0 as a complex multivariate Gaussian distribution, i.e., p(ṽ…”

Section: Low-dimensional Bayesian Model Of Registrationmentioning

confidence: 99%

Deep Learning for Regularization Prediction in Diffeomorphic Image Registration

Wang

Zhang

2020

Preprint

Self Cite

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This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration. We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. Our method significantly reduces the effort of parameter tuning, which is time and labor-consuming. To achieve the goal, we develop a predictive model based on deep convolutional neural networks (CNN) that learns the mapping between pairwise images and the regularization parameter of image registration. In contrast to previous methods that estimate such parameters in a high-dimensional image space, our model is built in an efficient bandlimited space with much lower dimensions. We demonstrate the effectiveness of our model on both 2D synthetic data and 3D real brain images. Experimental results show that our model not only predicts appropriate regularization parameters for image registration, but also improving the network training in terms of time and memory efficiency.

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Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification

Cited by 16 publications

References 18 publications

Factorisation-Based Image Labelling

Factorisation-Based Image Labelling

Factorisation-based Image Labelling

Deep Learning for Regularization Prediction in Diffeomorphic Image Registration

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