402 research outputs found
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
International audienceWe propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations. We model registration in a probabilistic and generative fashion, by applying a conditional variational autoencoder (CVAE) network. This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space. Most recent learning-based registration algorithms use supervised labels or deformation models, that miss important properties such as diffeomorphism and sufficiently regular deformation fields. In this work, we constrain transformations to be diffeomorphic by using a differentiable exponentiation layer with a symmetric loss function. We evaluated our method on 330 cardiac MR sequences and demonstrate robust intra-subject registration results comparable to two state-of-the-art methods but with more regular deformation fields compared to a recent learning-based algorithm. Our method reached a mean DICE score of 78.3% and a mean Hausdorff distance of 7.9mm. In two preliminary experiments, we illustrate the model's abilities to transport pathological deformations to healthy subjects and to cluster five diseases in the unsupervised deformation encoding space with a classification performance of 70%
A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy
In radiotherapy, the internal movement of organs between treatment sessions
causes errors in the final radiation dose delivery. Motion models can be used
to simulate motion patterns and assess anatomical robustness before delivery.
Traditionally, such models are based on principal component analysis (PCA) and
are either patient-specific (requiring several scans per patient) or
population-based, applying the same deformations to all patients. We present a
hybrid approach which, based on population data, allows to predict
patient-specific inter-fraction variations for an individual patient. We
propose a deep learning probabilistic framework that generates deformation
vector fields (DVFs) warping a patient's planning computed tomography (CT) into
possible patient-specific anatomies. This daily anatomy model (DAM) uses few
random variables capturing groups of correlated movements. Given a new planning
CT, DAM estimates the joint distribution over the variables, with each sample
from the distribution corresponding to a different deformation. We train our
model using dataset of 312 CT pairs from 38 prostate cancer patients. For 2
additional patients (22 CTs), we compute the contour overlap between real and
generated images, and compare the sampled and ground truth distributions of
volume and center of mass changes. With a DICE score of 0.86 and a distance
between prostate contours of 1.09 mm, DAM matches and improves upon PCA-based
models. The distribution overlap further indicates that DAM's sampled movements
match the range and frequency of clinically observed daily changes on repeat
CTs. Conditioned only on a planning CT and contours of a new patient without
any pre-processing, DAM can accurately predict CTs seen during following
treatment sessions, which can be used for anatomically robust treatment
planning and robustness evaluation against inter-fraction anatomical changes
Learning Conditional Deformable Templates with Convolutional Networks
We develop a learning framework for building deformable templates, which play
a fundamental role in many image analysis and computational anatomy tasks.
Conventional methods for template creation and image alignment to the template
have undergone decades of rich technical development. In these frameworks,
templates are constructed using an iterative process of template estimation and
alignment, which is often computationally very expensive. Due in part to this
shortcoming, most methods compute a single template for the entire population
of images, or a few templates for specific sub-groups of the data. In this
work, we present a probabilistic model and efficient learning strategy that
yields either universal or conditional templates, jointly with a neural network
that provides efficient alignment of the images to these templates. We
demonstrate the usefulness of this method on a variety of domains, with a
special focus on neuroimaging. This is particularly useful for clinical
applications where a pre-existing template does not exist, or creating a new
one with traditional methods can be prohibitively expensive. Our code and
atlases are available online as part of the VoxelMorph library at
http://voxelmorph.csail.mit.edu.Comment: NeurIPS 2019: Neural Information Processing Systems. Keywords:
deformable templates, conditional atlases, diffeomorphic image registration,
probabilistic models, neuroimagin
Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers
Image registration is an essential but challenging task in medical image
computing, especially for echocardiography, where the anatomical structures are
relatively noisy compared to other imaging modalities. Traditional
(non-learning) registration approaches rely on the iterative optimization of a
similarity metric which is usually costly in time complexity. In recent years,
convolutional neural network (CNN) based image registration methods have shown
good effectiveness. In the meantime, recent studies show that the
attention-based model (e.g., Transformer) can bring superior performance in
pattern recognition tasks. In contrast, whether the superior performance of the
Transformer comes from the long-winded architecture or is attributed to the use
of patches for dividing the inputs is unclear yet. This work introduces three
patch-based frameworks for image registration using MLPs and transformers. We
provide experiments on 2D-echocardiography registration to answer the former
question partially and provide a benchmark solution. Our results on a large
public 2D echocardiography dataset show that the patch-based MLP/Transformer
model can be effectively used for unsupervised echocardiography registration.
They demonstrate comparable and even better registration performance than a
popular CNN registration model. In particular, patch-based models better
preserve volume changes in terms of Jacobian determinants, thus generating
robust registration fields with less unrealistic deformation. Our results
demonstrate that patch-based learning methods, whether with attention or not,
can perform high-performance unsupervised registration tasks with adequate time
and space complexity. Our codes are available
https://gitlab.inria.fr/epione/mlp\_transformer\_registratio
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