Sparse annotation strategies for segmentation of short axis cardiac MRI

Short axis cardiac MRI segmentation is a well-researched topic, with excellent results achieved by state-of-the-art models in a supervised setting. However, annotating MRI volumes is time-consuming and expensive. Many different approaches (e.g. transfer learning, data augmentation, few-shot learning, etc.) have emerged in an effort to use fewer annotated data and still achieve similar performance as a fully supervised model. Nevertheless, to the best of our knowledge, none of these works focus on which slices of MRI volumes are most important to annotate for yielding the best segmentation results. In this paper, we investigate the effects of training with sparse volumes, i.e. reducing the number of cases annotated, and sparse annotations, i.e. reducing the number of slices annotated per case. We evaluate the segmentation performance using the state-of-the-art nnU-Net model on two public datasets to identify which slices are the most important to annotate. We have shown that training on a significantly reduced dataset (48 annotated volumes) can give a Dice score greater than 0.85 and results comparable to using the full dataset (160 and 240 volumes for each dataset respectively). In general, training on more slice annotations provides more valuable information compared to training on more volumes. Further, annotating slices from the middle of volumes yields the most beneficial results in terms of segmentation performance, and the apical region the worst. When evaluating the trade-off between annotating volumes against slices, annotating as many slices as possible instead of annotating more volumes is a better strategy

Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains

Deep generative models have emerged as influential instruments for data generation and manipulation. Enhancing the controllability of these models by selectively modifying data attributes has been a recent focus. Variational Autoencoders (VAEs) have shown promise in capturing hidden attributes but often produce blurry reconstructions. Controlling these attributes through different imaging domains is difficult in medical imaging. Recently, Soft Introspective VAE leverage the benefits of both VAEs and Generative Adversarial Networks (GANs), which have demonstrated impressive image synthesis capabilities, by incorporating an adversarial loss into VAE training. In this work, we propose the Attributed Soft Introspective VAE (Attri-SIVAE) by incorporating an attribute regularized loss, into the Soft-Intro VAE framework. We evaluate experimentally the proposed method on cardiac MRI data from different domains, such as various scanner vendors and acquisition centers. The proposed method achieves similar performance in terms of reconstruction and regularization compared to the state-of-the-art Attributed regularized VAE but additionally also succeeds in keeping the same regularization level when tested on a different dataset, unlike the compared method

Statistical learning of interactions between cardiac shape and deformation

En routine clinique, les méthodes d'imagerie permettent d'extraire des indices caractérisant la fonction cardiaque et d'établir un diagnostic. Dans le cas de l'insuffisance cardiaque, un remodelage cardiaque se produit très souvent. Plusieurs aspects de la morphologie et la fonction sont affectés, notamment des anomalies de forme et de déformation peuvent apparaître. De plus, des interactions entre ces deux aspects ont été mises en évidence. Ces interactions peuvent être difficilement analysées par des indices scalaires qui décrivent un comportement global. L'imagerie médicale est en mesure de fournir des représentations de hautes-dimensions de ces descripteurs, c'est-à-dire une information locale et à plusieurs instants du cycle cardiaque ; elles sont cependant non exploitées en routine clinique à cause de la difficulté de leur analyse. Dans ce manuscrit, nous explorons des approches de caractérisation plus fine du lien partiellement connu entre la forme et la déformation cardiaques via des représentations hautes dimensions. Des méthodes d'anatomie computationnelle ou d'apprentissage de variétés permettent d'exploiter ces représentations hautes-dimensions individuelles et généraliser l'analyse à une population. Néanmoins, ces méthodes ne considèrent généralement qu'un aspect de la fonction cardiaque à la fois alors que plusieurs interagissent. Les méthodes intégrant plusieurs descripteurs ne prennent habituellement pas explicitement en compte le lien possible entre eux. Ce travail comporte trois principales contributions. Premièrement, nous proposons une stratégie pour caractériser les interactions entre la forme et la déformation cardiaques évaluées par des descripteurs hautes dimensions et démontrons sa pertinence pour plusieurs pathologies du ventricule droit. Cette stratégie est basée sur une méthode d'apprentissage non-linéaire (alignement de variétés multiples) et utilisée ici pour caractériser un lien partiellement connu alors qu'elle a été jusqu'à présent appliquée à des descripteurs évoluant dans la même variété, et pour lesquels le lien existe naturellement. Deuxièmement, nous avons évalué le bénéfice de prendre en compte le lien entre les descripteurs en étudiant les différences avec une approche qui considère chaque descripteur individuellement et d'autres approches prenant en compte plusieurs descripteurs. Pour finir, l'étude de l'influence des descripteurs de forme/déformation et de stratégies de normalisation sur notre approche a mis en valeur un possible biais introduit par les choix faits et montré que le choix approprié dépend de l'application visée. Cette thèse montre la pertinence d'utiliser l'alignement de variétés pour considérer le lien partiellement connu entre forme et déformation cardiaques en l'illustrant dans l'étude comparée de plusieurs pathologies du ventricule droit. Ces analyses ouvrent la porte à l'exploitation de ces espaces cohérents pour des challenges plus applicatifs comme la quantification de risques.In clinical routine, medical imaging allows to extract descriptors or scalars characterizing the cardiac function and to establish a diagnosis. In heart failure, cardiac remodeling often occurs and several aspects of morphology and function are affected during this phenomenon, which can include shape and deformation abnormalities. In addition, interactions between these two aspects have been demonstrated structurally or related to certain cardiac pathologies. These interactions are difficult to analyze by simple scalar indices that generally describe a global behavior. Medical imaging is able to provide high-dimensional representations of these descriptors, i.e., regional/local information at several instants of the cardiac cycle. However, they are not exploited in clinical routine because of the lack of time, the lack of consensus, and the difficulty of their analysis. In this manuscript, we explore approaches to further characterize the partially-known link between cardiac shape and deformation via high-dimensional representations of both aspects. Computational anatomy or manifold learning methods allow to exploit these individual high-dimensional representations and generalize the analysis to a population. However, these methods generally consider only one aspect of cardiac function at a time, whereas several of them can interact. Methods incorporating multiple descriptors usually do not explicitly consider the possible link between them. This work has three main contributions. First, we propose a strategy to characterize the interactions between cardiac shape and deformation assessed by high-dimensional descriptors and demonstrate its relevance for several right ventricular pathologies. This strategy is based on a non-linear learning method (Multiple Manifold Alignment) and is used here to characterize a partially-known link, whereas it has been applied so far to descriptors belonging to the same manifold, and for which the link naturally exists. Secondly, we evaluated the benefit of taking into account the link between descriptors by studying the differences with an approach that considers each descriptor individually and other approaches that consider several descriptors jointly. Finally, the study of the influence of shape/deformation descriptors and normalization strategies on our approach has highlighted a possible bias introduced by the choices made and shown that the appropriate choice depends on the targeted application. This thesis shows the relevance of using manifold alignment to consider the partially-known link between shape and cardiac deformation by illustrating it in the comparative study of several right ventricular diseases. These analyses open the door to the exploitation of these coherent latent spaces for more applicative challenges such as risk quantification

Apprentissage statistique des interactions entre forme et déformation cardiaques

In clinical routine, medical imaging allows to extract descriptors or scalars characterizing the cardiac function and to establish a diagnosis. In heart failure, cardiac remodeling often occurs and several aspects of morphology and function are affected during this phenomenon, which can include shape and deformation abnormalities. In addition, interactions between these two aspects have been demonstrated structurally or related to certain cardiac pathologies. These interactions are difficult to analyze by simple scalar indices that generally describe a global behavior. Medical imaging is able to provide high-dimensional representations of these descriptors, i.e., regional/local information at several instants of the cardiac cycle. However, they are not exploited in clinical routine because of the lack of time, the lack of consensus, and the difficulty of their analysis. In this manuscript, we explore approaches to further characterize the partially-known link between cardiac shape and deformation via high-dimensional representations of both aspects. Computational anatomy or manifold learning methods allow to exploit these individual high-dimensional representations and generalize the analysis to a population. However, these methods generally consider only one aspect of cardiac function at a time, whereas several of them can interact. Methods incorporating multiple descriptors usually do not explicitly consider the possible link between them. This work has three main contributions. First, we propose a strategy to characterize the interactions between cardiac shape and deformation assessed by high-dimensional descriptors and demonstrate its relevance for several right ventricular pathologies. This strategy is based on a non-linear learning method (Multiple Manifold Alignment) and is used here to characterize a partially-known link, whereas it has been applied so far to descriptors belonging to the same manifold, and for which the link naturally exists. Secondly, we evaluated the benefit of taking into account the link between descriptors by studying the differences with an approach that considers each descriptor individually and other approaches that consider several descriptors jointly. Finally, the study of the influence of shape/deformation descriptors and normalization strategies on our approach has highlighted a possible bias introduced by the choices made and shown that the appropriate choice depends on the targeted application. This thesis shows the relevance of using manifold alignment to consider the partially-known link between shape and cardiac deformation by illustrating it in the comparative study of several right ventricular diseases. These analyses open the door to the exploitation of these coherent latent spaces for more applicative challenges such as risk quantification.En routine clinique, les méthodes d'imagerie permettent d'extraire des indices caractérisant la fonction cardiaque et d'établir un diagnostic. Dans le cas de l'insuffisance cardiaque, un remodelage cardiaque se produit très souvent. Plusieurs aspects de la morphologie et la fonction sont affectés, notamment des anomalies de forme et de déformation peuvent apparaître. De plus, des interactions entre ces deux aspects ont été mises en évidence. Ces interactions peuvent être difficilement analysées par des indices scalaires qui décrivent un comportement global. L'imagerie médicale est en mesure de fournir des représentations de hautes-dimensions de ces descripteurs, c'est-à-dire une information locale et à plusieurs instants du cycle cardiaque ; elles sont cependant non exploitées en routine clinique à cause de la difficulté de leur analyse. Dans ce manuscrit, nous explorons des approches de caractérisation plus fine du lien partiellement connu entre la forme et la déformation cardiaques via des représentations hautes dimensions. Des méthodes d'anatomie computationnelle ou d'apprentissage de variétés permettent d'exploiter ces représentations hautes-dimensions individuelles et généraliser l'analyse à une population. Néanmoins, ces méthodes ne considèrent généralement qu'un aspect de la fonction cardiaque à la fois alors que plusieurs interagissent. Les méthodes intégrant plusieurs descripteurs ne prennent habituellement pas explicitement en compte le lien possible entre eux. Ce travail comporte trois principales contributions. Premièrement, nous proposons une stratégie pour caractériser les interactions entre la forme et la déformation cardiaques évaluées par des descripteurs hautes dimensions et démontrons sa pertinence pour plusieurs pathologies du ventricule droit. Cette stratégie est basée sur une méthode d'apprentissage non-linéaire (alignement de variétés multiples) et utilisée ici pour caractériser un lien partiellement connu alors qu'elle a été jusqu'à présent appliquée à des descripteurs évoluant dans la même variété, et pour lesquels le lien existe naturellement. Deuxièmement, nous avons évalué le bénéfice de prendre en compte le lien entre les descripteurs en étudiant les différences avec une approche qui considère chaque descripteur individuellement et d'autres approches prenant en compte plusieurs descripteurs. Pour finir, l'étude de l'influence des descripteurs de forme/déformation et de stratégies de normalisation sur notre approche a mis en valeur un possible biais introduit par les choix faits et montré que le choix approprié dépend de l'application visée. Cette thèse montre la pertinence d'utiliser l'alignement de variétés pour considérer le lien partiellement connu entre forme et déformation cardiaques en l'illustrant dans l'étude comparée de plusieurs pathologies du ventricule droit. Ces analyses ouvrent la porte à l'exploitation de ces espaces cohérents pour des challenges plus applicatifs comme la quantification de risques

Apprentissage statistique des interactions entre forme et déformation cardiaques

In clinical routine, medical imaging allows to extract descriptors or scalars characterizing the cardiac function and to establish a diagnosis. In heart failure, cardiac remodeling often occurs and several aspects of morphology and function are affected during this phenomenon, which can include shape and deformation abnormalities. In addition, interactions between these two aspects have been demonstrated structurally or related to certain cardiac pathologies. These interactions are difficult to analyze by simple scalar indices that generally describe a global behavior. Medical imaging is able to provide high-dimensional representations of these descriptors, i.e., regional/local information at several instants of the cardiac cycle. However, they are not exploited in clinical routine because of the lack of time, the lack of consensus, and the difficulty of their analysis. In this manuscript, we explore approaches to further characterize the partially-known link between cardiac shape and deformation via high-dimensional representations of both aspects. Computational anatomy or manifold learning methods allow to exploit these individual high-dimensional representations and generalize the analysis to a population. However, these methods generally consider only one aspect of cardiac function at a time, whereas several of them can interact. Methods incorporating multiple descriptors usually do not explicitly consider the possible link between them. This work has three main contributions. First, we propose a strategy to characterize the interactions between cardiac shape and deformation assessed by high-dimensional descriptors and demonstrate its relevance for several right ventricular pathologies. This strategy is based on a non-linear learning method (Multiple Manifold Alignment) and is used here to characterize a partially-known link, whereas it has been applied so far to descriptors belonging to the same manifold, and for which the link naturally exists. Secondly, we evaluated the benefit of taking into account the link between descriptors by studying the differences with an approach that considers each descriptor individually and other approaches that consider several descriptors jointly. Finally, the study of the influence of shape/deformation descriptors and normalization strategies on our approach has highlighted a possible bias introduced by the choices made and shown that the appropriate choice depends on the targeted application. This thesis shows the relevance of using manifold alignment to consider the partially-known link between shape and cardiac deformation by illustrating it in the comparative study of several right ventricular diseases. These analyses open the door to the exploitation of these coherent latent spaces for more applicative challenges such as risk quantification.En routine clinique, les méthodes d'imagerie permettent d'extraire des indices caractérisant la fonction cardiaque et d'établir un diagnostic. Dans le cas de l'insuffisance cardiaque, un remodelage cardiaque se produit très souvent. Plusieurs aspects de la morphologie et la fonction sont affectés, notamment des anomalies de forme et de déformation peuvent apparaître. De plus, des interactions entre ces deux aspects ont été mises en évidence. Ces interactions peuvent être difficilement analysées par des indices scalaires qui décrivent un comportement global. L'imagerie médicale est en mesure de fournir des représentations de hautes-dimensions de ces descripteurs, c'est-à-dire une information locale et à plusieurs instants du cycle cardiaque ; elles sont cependant non exploitées en routine clinique à cause de la difficulté de leur analyse. Dans ce manuscrit, nous explorons des approches de caractérisation plus fine du lien partiellement connu entre la forme et la déformation cardiaques via des représentations hautes dimensions. Des méthodes d'anatomie computationnelle ou d'apprentissage de variétés permettent d'exploiter ces représentations hautes-dimensions individuelles et généraliser l'analyse à une population. Néanmoins, ces méthodes ne considèrent généralement qu'un aspect de la fonction cardiaque à la fois alors que plusieurs interagissent. Les méthodes intégrant plusieurs descripteurs ne prennent habituellement pas explicitement en compte le lien possible entre eux. Ce travail comporte trois principales contributions. Premièrement, nous proposons une stratégie pour caractériser les interactions entre la forme et la déformation cardiaques évaluées par des descripteurs hautes dimensions et démontrons sa pertinence pour plusieurs pathologies du ventricule droit. Cette stratégie est basée sur une méthode d'apprentissage non-linéaire (alignement de variétés multiples) et utilisée ici pour caractériser un lien partiellement connu alors qu'elle a été jusqu'à présent appliquée à des descripteurs évoluant dans la même variété, et pour lesquels le lien existe naturellement. Deuxièmement, nous avons évalué le bénéfice de prendre en compte le lien entre les descripteurs en étudiant les différences avec une approche qui considère chaque descripteur individuellement et d'autres approches prenant en compte plusieurs descripteurs. Pour finir, l'étude de l'influence des descripteurs de forme/déformation et de stratégies de normalisation sur notre approche a mis en valeur un possible biais introduit par les choix faits et montré que le choix approprié dépend de l'application visée. Cette thèse montre la pertinence d'utiliser l'alignement de variétés pour considérer le lien partiellement connu entre forme et déformation cardiaques en l'illustrant dans l'étude comparée de plusieurs pathologies du ventricule droit. Ces analyses ouvrent la porte à l'exploitation de ces espaces cohérents pour des challenges plus applicatifs comme la quantification de risques

Characterizing interactions between cardiac shape and deformation by non-linear manifold learning

International audienceIn clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population

Which anatomical directions to quantify local right ventricular strain in 3D echocardiography?

International audienceTechnological advances in image quality and post-processing have led to the better clinical adoption of 3D echocardiography to quantify cardiac function. However, the right ventricle (RV) raises specific challenges due to its specific half-moon shape, which led to a lack of consensus regarding the estimation of RV motion and deformation locally. In this paper, we detail three ways to estimate local anatomically-relevant directions at each point of the RV surface, in 3D, and the resulting Green-Lagrange strain projected along these directions. Using a database of RV surface meshes extracted from 3D echocardiographic sequences from 100 control subjects, we quantified differences between these strategies in terms of local anatomical directions and local strain, both at the individual and population levels. For the latter, we used a specific dimensionality reduction technique to align the latent spaces encoding the strain patterns obtained from different computations of the anatomical directions. Differences were subtle but visible at specific regions of the RV and partially interpretable, although their impact on the population latent representation was low, which sets a preliminary quantitative basis to discuss these computation standards

Investigation of the impact of normalization on the study of interactions between myocardial shape and deformation

International audienceMyocardial shape and deformation are two relevant descriptors for the study of cardiac function and can undergo strong interactions depending on diseases. Manifold learning provides low dimensional representations of these high-dimensional descriptors, but the choice of normalization can strongly affect the analysis. Besides, whether the shape normalization should include a scale factor is still an open question. In this paper, we investigate the influence of normalization choices on the study of the interactions between cardiac shape and deformation using Multiple Manifold Learning, a dimensionality reduction method that considers inter-and intra-descriptors link between samples. By studying the main variations of two different shape normalizations (one including scaling, the other one not) we observed that the scaled normalization concentrates variations of a given physiological characteristic on only one mode. The influence of the associated choice of the deformation normalization was evaluated by quantifying differences between the estimated low-dimensional spaces (one for each choice against a combination of both), revealing potential analysis biases that may arise depending on such choices

Which anatomical directions to quantify local right ventricular strain in 3D echocardiography?

International audienceTechnological advances in image quality and post-processing have led to the better clinical adoption of 3D echocardiography to quantify cardiac function. However, the right ventricle (RV) raises specific challenges due to its specific half-moon shape, which led to a lack of consensus regarding the estimation of RV motion and deformation locally. In this paper, we detail three ways to estimate local anatomically-relevant directions at each point of the RV surface, in 3D, and the resulting Green-Lagrange strain projected along these directions. Using a database of RV surface meshes extracted from 3D echocardiographic sequences from 100 control subjects, we quantified differences between these strategies in terms of local anatomical directions and local strain, both at the individual and population levels. For the latter, we used a specific dimensionality reduction technique to align the latent spaces encoding the strain patterns obtained from different computations of the anatomical directions. Differences were subtle but visible at specific regions of the RV and partially interpretable, although their impact on the population latent representation was low, which sets a preliminary quantitative basis to discuss these computation standards