26 research outputs found

    Atlas construction and image analysis using statistical cardiac models

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    International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

    Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

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    We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.Comment: accepted at IEEE TM

    Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

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    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art

    Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

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    International audienceWe propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic spacethe motion matrix-which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation

    Le recalage robuste d’images médicales et la modélisation du mouvement basée sur l’apprentissage profond

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    This thesis presents new computational tools for quantifying deformations and motion of anatomical structures from medical images as required by a large variety of clinical applications. Generic deformable registration tools are presented that enable deformation analysis useful for improving diagnosis, prognosis and therapy guidance. These tools were built by combining state-of-the-art medical image analysis methods with cutting-edge machine learning methods.First, we focus on difficult inter-subject registration problems. By learning from given deformation examples, we propose a novel agent-based optimization scheme inspired by deep reinforcement learning where a statistical deformation model is explored in a trial-and-error fashion showing improved registration accuracy. Second, we develop a diffeomorphic deformation model that allows for accurate multiscale registration and deformation analysis by learning a low-dimensional representation of intra-subject deformations. The unsupervised method uses a latent variable model in form of a conditional variational autoencoder (CVAE) for learning a probabilistic deformation encoding that is useful for the simulation, classification and comparison of deformations.Third, we propose a probabilistic motion model derived from image sequences of moving organs. This generative model embeds motion in a structured latent space, the motion matrix, which enables the consistent tracking of structures and various analysis tasks. For instance, it leads to the simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation.Finally, we demonstrate the importance of the developed tools in a clinical application where the motion model is used for disease prognosis and therapy planning. It is shown that the survival risk for heart failure patients can be predicted from the discriminative motion matrix with a higher accuracy compared to classical image-derived risk factors.Cette thèse présente de nouveaux outils informatiques pour quantifier les déformations et le mouvement de structures anatomiques à partir d’images médicales dans le cadre d’une grande variété d’applications cliniques. Des outils génériques de recalage déformable sont présentés qui permettent l’analyse de la déformation de tissus anatomiques pour améliorer le diagnostic, le pronostic et la thérapie. Ces outils combinent des méthodes avancées d’analyse d’images médicales avec des méthodes d’apprentissage automatique performantes.Dans un premier temps, nous nous concentrons sur les problèmes de recalages inter-sujets difficiles. En apprenant à partir d’exemples de déformation donnés, nous proposons un nouveau schéma d’optimisation basé sur un agent inspiré de l’apprentissage par renforcement profond dans lequel un modèle de déformation statistique est exploré de manière itérative montrant une précision améliorée de recalage. Dans un second temps, nous développons un modèle de déformation difféomorphe qui permet un recalage multi-échelle précis et une analyse de déformation en apprenant une représentation de faible dimension des déformations intra-sujet. La méthode non supervisée utilise un modèle de variable latente sous la forme d’un autoencodeur variationnel conditionnel (CVAE) pour apprendre une représentation probabiliste des déformations qui est utile pour la simulation, la classification et la comparaison des déformations. Troisièmement, nous proposons un modèle de mouvement probabiliste dérivé de séquences d’images d’organes en mouvement. Ce modèle génératif décrit le mouvement dans un espace latent structuré, la matrice de mouvement, qui permet le suivi cohérent des structures ainsi que l’analyse du mouvement. Ainsi cette approche permet la simulation et l’interpolation de modèles de mouvement réalistes conduisant à une acquisition et une augmentation des données plus rapides.Enfin, nous démontrons l’intérêt des outils développés dans une application clinique où le modèle de mouvement est utilisé pour le pronostic de maladies et la planification de thérapies. Il est démontré que le risque de survie des patients souffrant d’insuffisance cardiaque peut être prédit à partir de la matrice de mouvement discriminant avec une précision supérieure par rapport aux facteurs de risque classiques dérivés de l’image

    Parallel transport, a central tool in geometric statistics for computational anatomy: Application to cardiac motion modeling

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    International audienceTransporting the statistical knowledge regressed in the neighbourhood of a point to a different but related place (transfer learning) is important for many applications. In medical imaging, cardiac motion modelling and structural brain changes are two such examples: for a group-wise statistical analysis, subjectspecific longitudinal deformations need to be transported in a common template anatomy. In geometric statistics, the natural (parallel) transport method is defined by the integration of a Riemannian connection which specifies how tangent vectors are compared at neighbouring points. In this process, the numerical accuracy of the transport method is critical. Discrete methods based on iterated geodesic parallelograms inspired by Schild’s ladder were shown to be very efficient and apparently stable in practice. In this chapter, we show that ladder methods are actually second order schemes, even with numerically approximated geodesics. We also propose a new original algorithm to implement these methods in the context of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework that endows the space of diffeomorphisms with a right-invariant RKHS metric. When applied to the motion modelling of the cardiac right ventricle under pressure or volume overload, the method however exhibits unexpected effects in the presence of very large volume differences between subjects. We first investigate an intuitive rescaling of the modulus after parallel transport to preserve the ejection fraction. The surprisingly simple scaling/volume relationship that we obtain suggests to decouples the volume change from the deformation directly within the LDMMM metric. The parallel transport of cardiac trajectories with this new metric now reveals statistical insights into the dynamics of each disease. This example shows that parallel transport could become a tool of choice for data-driven metric optimization

    Coronary motion modelling for CTA to X-ray angiography registration

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    Coronary motion modelling for CTA to X-ray angiography registration

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    Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

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    Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

    Non-rigid multi-frame registration of cell nuclei in live cell microscopy image data

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    To gain a better understanding of cellular and molecular processes it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. For accurate quantification of the subcellular particle motion, compensation of the motion and deformation of the cell nucleus is required. This thesis deals with non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images. We developed two multi-frame non-rigid registration approaches which simultaneously exploit information from multiple consecutive frames of an image sequence to improve the registration accuracy. The multi-frame registration approaches are based on local optic flow estimation, use information from multiple consecutive images, and take into account computed transformations from previous time steps. The first approach comprises three intensity-based variants and two different temporal weighting schemes. The second approach determines diffeomorphic transformations in the log-domain which allows efficient computation of the inverse transformations. We use a temporally weighted mean image which is constructed based on inverse transformations and multiple consecutive frames. In addition, we employ a flow boundary preserving method for regularization of computed deformation vector fields. Both multi-frame registration approaches have been successfully applied to 2D and 3D synthetic as well as real live cell microscopy image sequences. We have performed an extensive quantitative evaluation of our approaches and compared their performance with previous non-rigid pairwise, multi-frame, and temporal groupwise registration approaches
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