Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net

International audienceSegmentation of the myocardium is a key step for image guided diagnosis in many cardiac diseases. In this article, we propose an automatic multi-atlas segmentation framework which relies on a very fast registration algorithm trained with convolutional neural networks. The speed of this registration method allows us to use a high number of templates in the multi-atlas segmentation while remaining computation-ally tractable. The performance of the propose approach is evaluated on a dataset of 100 end-diastolic and end-systolic MRI images of the STACOM 2017 Automated Cardiac Diagnosis Challenge (ACDC)

Barycentric Subspace Analysis: a new Symmetric Group-wise Paradigm for Cardiac Motion Tracking

International audienceIn this paper, we propose a novel approach to study cardiac motion in 4D image sequences. Whereas traditional approaches rely on the registration of the whole sequence with respect to the first frame usually corresponding to the end-diastole (ED) image, we define a more generic basis using the barycentric subspace spanned by a number of references images of the sequence. These subspaces are implicitly defined as the locus of points which are weighted Karcher means of k + 1 references images. We build such subspace on the cardiac motion images, to get a Barycentric Template that is no longer defined by a single image but parametrized by coefficients: the barycentric coordinates. We first show that the barycentric coordinates-the coefficients of the projection of the motion during a cardiac sequence-define a meaningful signature for group-wise analysis of dynamics and can efficiently separate two populations. Then, we use the barycentric template as a prior for regularization in cardiac motion tracking, efficiently reducing the error of tracking between end-systole and end-diastole by almost 40% as well as the error of the evaluation of the ejection fraction. Finally, to best exploit the fact that multiple reference images allow to reduce the registration displacement , we derived a symmetric and transitive registration that can be used both for frame-to-frame and frame-to-reference registration and further improves the accuracy of the registration

Low-Dimensional Representation of Cardiac Motion Using Barycentric Subspaces: a New Group-Wise Paradigm for Estimation, Analysis, and Reconstruction

International audienceOne major challenge when trying to build low-dimensional representation of the cardiac motion is its natural circular pattern during a cycle, therefore making the mean image a poor descriptor of the whole sequence. Therefore, traditional approaches for the analysis of the cardiac deformation use one specific frame of the sequence-the end-diastolic (ED) frame-as a reference to study the whole motion. Consequently, this methodology is biased by this empirical choice. Moreover, the ED image might be a poor reference when looking at large deformation for example at the end-systolic (ES) frame. In this paper, we propose a novel approach to study cardiac motion in 4D image sequences using low-dimensional subspace analysis. Instead of building subspaces relying on a mean value we use a novel type of subspaces called Barycentric Subspaces which are implicitly defined as the weighted Karcher means of k + 1 reference images instead of being defined with respect to one reference image. In the first part of this article, we introduce the methodological framework and the algorithms used to manipulate images within these new subspaces: how to compute the projection of a given image on the Barycentric Subspace with its coordinates, and the opposite operation of computing an image from a set of references and coordinates. Then we show how this framework can be applied to cardiac motion problems and lead to significant improvements over the single reference method. Firstly, by computing the low-dimensional representation of two populations we show that the parameters extracted correspond to relevant cardiac motion features leading to an efficient representation and discrimination of both groups. Secondly, in motion estimation, we use the projection on this low-dimensional subspace as an additional prior on the regularization in cardiac motion tracking, efficiently reducing the error of the registration between the ED and ES by almost 30%. We also derive a symmetric and transitive formulation of the registration that can be used both for frame-to-frame and frame-to-reference registration. Finally, we look at the reconstruction of the images using our proposed low-dimensional representation and show that this Figure 1: (Left): representation of the classical methodology. A mean point (image in green) is computed and the statistical analysis for each data is done with respect to this point. The reference is not a point of the data. (Right): our proposed multi-reference approach. The subspace is not built based on a central point but each of the data is analyzed with respect to a set of references (green). multi-references method using Barycentric Subspaces performs better than traditional approaches based on a single reference

Combination of Polyaffine Transformations and Supervised Learning for the Automatic Diagnosis of LV Infarct

International audienceIn this article, we present an application of the polyaffine transformations to classify a population of hearts with myocardial infarction. Polyaffine transformations aim at representing motion by the combination of a limited number of affine transformations defined locally on a regional division of the space. We show that these transformations not only serve as a first (non-learnt) dimension reduction, but also allow to interpret each of the parameters and relate them to known clinical parameters. Then, we use standard supervised learning algorithms on these parameters to classify the population between infarcted and non-infarcted subjects. The method is applied on the STACOM statistical shape modeling labeled data consisting of 200 cases, comprising the same number of healthy subjects and patients with infarct. We train classifiers using different standard machine learning algorithms. Finally, we validate our method with 10-fold cross-validation and get more than 95% of correct classification on yet-unseen data. The method is promising and ready to be tested on the remaining 200 test cases of the challenge

Highly Reduced Model of the Cardiac Function for Fast Simulation

International audienceIn this article we present a drastic dimension reduction method to link the biophysical parameters of an electromechanical model of the heart with a compact representation of cardiac motion. Our approach relies on a projection of the displacement fields along the whole cardiac motion to the space of reduced-polyaffine transformations. Using these transformations, not only we describe the motion using a very small number of parameters but we show that each of these parameters has a physiological meaning. Moreover, using a PLS regression on a learning set made of a large number of simulations, we are able to find which of the input parameters of the model most impact the motion and what are the main relations mapping the polyaffine representation to the parameters of the model. We illustrate the potential of this method for building a direct and very fast model characterized by a highly reduced number of parameters

Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer

We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enforcing their independence with HCR features by minimizing their mutual information. The resulting DLR features can be combined with hand-crafted ones and leveraged by a classifier to predict early markers of cancer. We illustrate our method on four early markers of pancreatic cancer and validate it on a large independent test set. Our results highlight the value of combining non-redundant DLR and HCR features, as evidenced by an improvement in the Area Under the Curve compared to baseline methods that do not address redundancy or solely rely on HCR features.Comment: CaPTion workshop MICCAI 202

Prediction of Post-Ablation Outcome in Atrial Fibrillation Using Shape Parameterization and Partial Least Squares Regression

International audienceTo analyze left atrial remodeling may reveal shape features related to post-ablation outcome in atrial fibrillation, which helps in identifying suitable candidates before ablation. In this article, we propose an application of diffeomorphometry and partial least squares regression to address this problem. We computed a template of left atrial shape in control group and then encoded the shapes in atrial fibrillation with a large set of parameters representing their diffeomorphic deformation. We applied a two-step partial least squares regression. The first step eliminates the influence of atrial volume in shape parameters. The second step links deformations directly to post-ablation recurrence and derives a few principle modes of deformation, which are unrelated to volume change but are involved in post-ablation recurrence. These modes contain information on ablation success due to shape differences, resulting from remodeling or influencing ablation procedure. Some details are consistent with the most complex area of ablation in clinical practice. Finally, we compared our method against the left atrial volume index by quantifying the risk of post-ablation recurrence within six months. Our results show that we get better prediction capabilities (area under receiver operating characteristic curves (AUC = 0.73) than left atrial dilation (AUC = 0.47), which outperforms the current state of the art

Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

International audienceComputer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1

A lightweight method to simulate low-dose DSC-perfusion sequences

International audienc