Deep Learning Formulation of ECGI Integrating Image & Signal Information with Data-driven Regularisation

International audienceAims: Electrocardiographic Imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. Methods: We propose a Deep Learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational Autoencoders (CVAE) using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies. Results: We evaluated our model by training it on five different cardiac anatomies (5 000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. Conclusion: The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from body surface potential. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results

Interpretable Prediction of Post-Infarct Ventricular Arrhythmia using Graph Convolutional Network

International audienceHeterogeneity of left ventricular (LV) myocardium infarction scar plays an important role as anatomical substrate in ventricular arrhythmia (VA) mechanism. LV myocardium thinning, as observed on cardiac computed tomography (CT), has been shown to correlate with LV myocardial scar and with abnormal electrical activity. In this project, we propose an automatic pipeline for VA prediction, based on CT images, using a Graph Convolutional Network (GCN). The pipeline includes the segmentation of LV masks from the input CT image, the short-axis orientation reformatting, LV myocardium thickness computation and mid-wall surface mesh generation. An average LV mesh was computed and fitted to every patient in order to use the same number of vertices with point-to-point correspondence. The GCN model was trained using the thickness value as the node feature and the atlas edges as the adjacency matrix. This allows the model to process the data on the 3D patient anatomy and bypass the “grid” structure limitation of the traditional convolutional neural network. The model was trained and evaluated on a dataset of 600 patients (27% VA), using 451 (3/4) and 149 (1/4) patients as training and testing data, respectively. The evaluation results showed that the graph model (81% accuracy) outperformed the clinical baseline (67%), the left ventricular ejection fraction, and the scar size (73%). We further studied the interpretability of the trained model using LIME and integrated gradients and found promising results on the personalised discovering of the specific regions within the infarct area related to the arrhythmogenesis

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice - VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.Comment: Submitted to Medical Image Analysi

Apprentissage profond sur de grandes bases de données cliniques pour la prédiction des arythmies cardiaques à partir de l'imagerie

Sudden cardiac death (SCD) is a major health concern responsible for up to a yearly mortality of 400,000 in the US, and a similar figure in Europe. SCD is a consequence of the sudden and total stop of cardiac activity, also known as the sudden cardiac arrest (SCA). A study monitoring the cardiac activity has linked the SCA to ventricular arrhythmia (VA), which is responsible for up to 70% of all recorded SCAs. The preventive approach to VA and the patient risk of the SCD is the implantable cardioverter defibrillator (ICD), while catheter-based radiofrequency ablation (RFA) is a potentially curative therapy. ICD continuously monitors the cardiac electrical activity and is programmed to deliver appropriate electrical shock(s) in order to stop any detected VA episodes. However, a main limitation is the patient selection criteria, often based only on the left ventricular ejection fraction (LVEF) index, which suffers from both large number of false positive and negative identifications. RFA is an interventional procedure to eliminate the substrate of reentry circuits, the primary cause in scar-related VA, which can effectively prevent future episode of VA. Nonetheless, this procedure is a very time consuming, high-risk and error-prone procedure, for which the major issue remains the inaccurate substrate localization.Based on novel Deep Learning (DL) methods, we studied the heterogeneity of the post-infarct myocardial scar in the left ventricle (LV) and its relation to VA, using a large retrospective dataset of 600 cardiac CT images. In the context of substrate localization and RFA guidance, thinner LV wall within the scar zone has been shown to have slow conduction properties. Our primary goal is to build a DL prediction model to analyse the LV wall thickness, thus improving the patients selection prior to the RFA treatment or a prevention strategy. The secondary goal is to explore the DL prediction model explainability. The idea is to pinpoint the specific regions on the model input that are most influential to the model prediction, which in the context of VA prediction could be translated into a personalised identification of arrhythmogenic regions.We started by implementing a fully automatic pipeline to transform the input (i.e., 3D CT cardiac images) into a 2D bullseye representation of the LV thickness value. For the VA prediction model, we built a conditional variational autoencoder classification (CVAE-Class) model. To study the explainability of the model, we employed the GradCAM++ method. We successfully showed that CVAE-Class model was able to achieve higher accuracy VA predictions compared to the available clinical markers, including the LVEF. The class activation map generated with GradCAM++ showed a high correlation of the high coefficient regions with the thinning regions, which solidified the relation between of LV wall thinning and arrhythmogenesis. We further explored the graph neural network method, which is shown to achieved better performance than the CVAE-Class model. We explored the explainability of the graph model using two methods: Local Interpretable Model-agnostic Explanation (LIME) and integrated gradients, which output smaller and more distinctive regions of high coefficient compared to the GradCAM++ method.Overall, this research work advances our knowledge by proposing a novel fully-automatic method to analyse the heterogeneity of the LV wall thinning, which allowed us to predict more robustly scar-related VA risk, compared to the current clinical markers. In particular, the inclusion of explainability in the output is a critical feature leading to a better transparency to the prediction model. Moreover, the subsequent improvement of our method via integration of LIME and integrated gradient, provides an optimistic perspective for the translation of this work into the clinical routine for automatic localisation of the arrhythmogenic substrates of ablation therapy.La mort subite (MS) est un problème de santé majeur, responsable d'une mortalité annuelle de 400 000 personnes aux États-Unis et d'un chiffre similaire en Europe. La mort subite est la conséquence de l'arrêt soudain et total de l'activité cardiaque, également connu sous le nom d'arrêt cardiaque soudain (ACS). Une étude de surveillance de l'activité cardiaque a établi un lien entre l'ACS et l'arythmie ventriculaire (AV), qui est responsable de 70 % de tous les ACS enregistrés. L'approche préventive d'arythmie ventriculaire est le défibrillateur automatique implantable (DAI), tandis que l'ablation par radiofréquence (AR) est une thérapie potentiellement curative. Le DAI surveille l'activité électrique cardiaque et est programmé pour délivrer des chocs électriques afin d'arrêter l'épisode d'AV. Cependant, les critères de sélection des patients, souvent basés sur l'indice de la fraction d'éjection du ventricule gauche (FEVG), constituent une limite importante, avec un grand nombre de faux positifs et négatifs. La AR est une procédure interventionnelle qui permet d'éliminer le substrat des circuits de réentrée, la cause principale de l'AV liée à la cicatrice. Néanmoins, il s'agit d'une procédure longue et à haut risque, dont le principal problème reste la localisation imprécise du substrat.En utilisant des méthodes Deep Learning (DL), nous avons étudié l'hétérogénéité de la cicatrice myocardique post-infarctus dans le ventricule gauche (VG) et sa relation avec l'AV, avec des données rétrospectives de 600 images de scanner cardiaque. Dans le contexte de la localisation du substrat et du guidage de la AR, il a été démontré que la paroi du VG plus mince dans la zone de la cicatrice a des propriétés de conduction lente. Notre objectif principal est de construire un modèle de prédiction pour analyser l'épaisseur du VG, améliorant ainsi la sélection des patients avant le traitement par AR ou une stratégie de prévention. L'objectif secondaire est d'explorer l'explicabilité du modèle. L'idée est d'identifier les régions spécifiques qui ont le plus d'influence sur la prédiction du modèle, ce qui, dans le contexte de la prédiction de l'AV, pourrait se traduire par une identification personnalisée des régions arythmogènes.Nous avons développer un pipeline automatique pour transformer l'entrée (les scanners cardiaques 3D) en une carte polaire de la valeur de l'épaisseur du VG. Pour le modèle de prédiction, nous avons construit le modèle conditional variational autoencoder classification (CVAE-Class). Pour étudier l'explicabilité du modèle, nous avons utilisé la méthode GradCAM++. Nous avons montré que le modèle CVAE-Class était capable d'obtenir des prédictions AV plus précises par rapport aux marqueurs cliniques, y compris la FEVG. La carte d'activation générée avec GradCAM++ a montré une forte corrélation entre les régions à fort coefficient et les régions amincies, ce qui a renforcé la relation entre l'amincissement de la paroi du ventricule gauche et l'arythmogenèse. Nous avons également étudié la méthode graph neural network, qui s'est avérée plus performante que le modèle CVAE-Class. Nous avons exploré l'explicabilité du modèle graphique à l'aide de deux méthodes : Local Interpretable Model-agnostic Explanation (LIME) et les integrated gradients, qui produisent des régions de coefficient élevé plus distinctes par rapport à la méthode GradCAM++.Ce travail de recherche fait progresser nos connaissances en proposant une nouvelle méthode automatique pour analyser l'hétérogénéité de l'amincissement du VG, qui nous a permis de prédire de manière plus robuste le risque d'AV lié à la cicatrice, par rapport aux marqueurs cliniques actuels. En particulier, l'inclusion de l'explicabilité dans la sortie conduit à une meilleure transparence du modèle de prédiction. De plus, l'amélioration ultérieure de notre méthode, offre une perspective optimiste pour la traduction de ce travail dans la localisation automatique des substrats arythmogènes du AR

Apprentissage profond sur de grandes bases de données cliniques pour la prédiction des arythmies cardiaques à partir de l'imagerie

Sudden cardiac death (SCD) is a major health concern responsible for up to a yearly mortality of 400,000 in the US, and a similar figure in Europe. SCD is a consequence of the sudden and total stop of cardiac activity, also known as the sudden cardiac arrest (SCA). A study monitoring the cardiac activity has linked the SCA to ventricular arrhythmia (VA), which is responsible for up to 70% of all recorded SCAs. The preventive approach to VA and the patient risk of the SCD is the implantable cardioverter defibrillator (ICD), while catheter-based radiofrequency ablation (RFA) is a potentially curative therapy. ICD continuously monitors the cardiac electrical activity and is programmed to deliver appropriate electrical shock(s) in order to stop any detected VA episodes. However, a main limitation is the patient selection criteria, often based only on the left ventricular ejection fraction (LVEF) index, which suffers from both large number of false positive and negative identifications. RFA is an interventional procedure to eliminate the substrate of reentry circuits, the primary cause in scar-related VA, which can effectively prevent future episode of VA. Nonetheless, this procedure is a very time consuming, high-risk and error-prone procedure, for which the major issue remains the inaccurate substrate localization.Based on novel Deep Learning (DL) methods, we studied the heterogeneity of the post-infarct myocardial scar in the left ventricle (LV) and its relation to VA, using a large retrospective dataset of 600 cardiac CT images. In the context of substrate localization and RFA guidance, thinner LV wall within the scar zone has been shown to have slow conduction properties. Our primary goal is to build a DL prediction model to analyse the LV wall thickness, thus improving the patients selection prior to the RFA treatment or a prevention strategy. The secondary goal is to explore the DL prediction model explainability. The idea is to pinpoint the specific regions on the model input that are most influential to the model prediction, which in the context of VA prediction could be translated into a personalised identification of arrhythmogenic regions.We started by implementing a fully automatic pipeline to transform the input (i.e., 3D CT cardiac images) into a 2D bullseye representation of the LV thickness value. For the VA prediction model, we built a conditional variational autoencoder classification (CVAE-Class) model. To study the explainability of the model, we employed the GradCAM++ method. We successfully showed that CVAE-Class model was able to achieve higher accuracy VA predictions compared to the available clinical markers, including the LVEF. The class activation map generated with GradCAM++ showed a high correlation of the high coefficient regions with the thinning regions, which solidified the relation between of LV wall thinning and arrhythmogenesis. We further explored the graph neural network method, which is shown to achieved better performance than the CVAE-Class model. We explored the explainability of the graph model using two methods: Local Interpretable Model-agnostic Explanation (LIME) and integrated gradients, which output smaller and more distinctive regions of high coefficient compared to the GradCAM++ method.Overall, this research work advances our knowledge by proposing a novel fully-automatic method to analyse the heterogeneity of the LV wall thinning, which allowed us to predict more robustly scar-related VA risk, compared to the current clinical markers. In particular, the inclusion of explainability in the output is a critical feature leading to a better transparency to the prediction model. Moreover, the subsequent improvement of our method via integration of LIME and integrated gradient, provides an optimistic perspective for the translation of this work into the clinical routine for automatic localisation of the arrhythmogenic substrates of ablation therapy.La mort subite (MS) est un problème de santé majeur, responsable d'une mortalité annuelle de 400 000 personnes aux États-Unis et d'un chiffre similaire en Europe. La mort subite est la conséquence de l'arrêt soudain et total de l'activité cardiaque, également connu sous le nom d'arrêt cardiaque soudain (ACS). Une étude de surveillance de l'activité cardiaque a établi un lien entre l'ACS et l'arythmie ventriculaire (AV), qui est responsable de 70 % de tous les ACS enregistrés. L'approche préventive d'arythmie ventriculaire est le défibrillateur automatique implantable (DAI), tandis que l'ablation par radiofréquence (AR) est une thérapie potentiellement curative. Le DAI surveille l'activité électrique cardiaque et est programmé pour délivrer des chocs électriques afin d'arrêter l'épisode d'AV. Cependant, les critères de sélection des patients, souvent basés sur l'indice de la fraction d'éjection du ventricule gauche (FEVG), constituent une limite importante, avec un grand nombre de faux positifs et négatifs. La AR est une procédure interventionnelle qui permet d'éliminer le substrat des circuits de réentrée, la cause principale de l'AV liée à la cicatrice. Néanmoins, il s'agit d'une procédure longue et à haut risque, dont le principal problème reste la localisation imprécise du substrat.En utilisant des méthodes Deep Learning (DL), nous avons étudié l'hétérogénéité de la cicatrice myocardique post-infarctus dans le ventricule gauche (VG) et sa relation avec l'AV, avec des données rétrospectives de 600 images de scanner cardiaque. Dans le contexte de la localisation du substrat et du guidage de la AR, il a été démontré que la paroi du VG plus mince dans la zone de la cicatrice a des propriétés de conduction lente. Notre objectif principal est de construire un modèle de prédiction pour analyser l'épaisseur du VG, améliorant ainsi la sélection des patients avant le traitement par AR ou une stratégie de prévention. L'objectif secondaire est d'explorer l'explicabilité du modèle. L'idée est d'identifier les régions spécifiques qui ont le plus d'influence sur la prédiction du modèle, ce qui, dans le contexte de la prédiction de l'AV, pourrait se traduire par une identification personnalisée des régions arythmogènes.Nous avons développer un pipeline automatique pour transformer l'entrée (les scanners cardiaques 3D) en une carte polaire de la valeur de l'épaisseur du VG. Pour le modèle de prédiction, nous avons construit le modèle conditional variational autoencoder classification (CVAE-Class). Pour étudier l'explicabilité du modèle, nous avons utilisé la méthode GradCAM++. Nous avons montré que le modèle CVAE-Class était capable d'obtenir des prédictions AV plus précises par rapport aux marqueurs cliniques, y compris la FEVG. La carte d'activation générée avec GradCAM++ a montré une forte corrélation entre les régions à fort coefficient et les régions amincies, ce qui a renforcé la relation entre l'amincissement de la paroi du ventricule gauche et l'arythmogenèse. Nous avons également étudié la méthode graph neural network, qui s'est avérée plus performante que le modèle CVAE-Class. Nous avons exploré l'explicabilité du modèle graphique à l'aide de deux méthodes : Local Interpretable Model-agnostic Explanation (LIME) et les integrated gradients, qui produisent des régions de coefficient élevé plus distinctes par rapport à la méthode GradCAM++.Ce travail de recherche fait progresser nos connaissances en proposant une nouvelle méthode automatique pour analyser l'hétérogénéité de l'amincissement du VG, qui nous a permis de prédire de manière plus robuste le risque d'AV lié à la cicatrice, par rapport aux marqueurs cliniques actuels. En particulier, l'inclusion de l'explicabilité dans la sortie conduit à une meilleure transparence du modèle de prédiction. De plus, l'amélioration ultérieure de notre méthode, offre une perspective optimiste pour la traduction de ce travail dans la localisation automatique des substrats arythmogènes du AR

Deep learning formulation of ECGI evaluated on clinical data

International audienceElectrocardiographic Imaging (ECGI) is an exceptional resource in cardiology practice and research, allowing for non-invasive assessment of local cardiac electrical activities, through the acquisition of ECGs signals acquired with multi-electrodes vests. This approach is largely based on solving an ill-posed inverse problem. However, to date, there is no method sufficiently convincing to solve the inverse problem, to establish ECGI as the clinical modality of choice. Previously, we proposed a deep learning (DL) based method for ECGI reconstruction by exploiting multimodal information and prior knowledge from previous cases. Tested with synthetic data, the method proved to be effective and convincing, but clinical validation is lacking

Outcome Prediction

International audienceThis chapter focuses on how we can best predict the future health of patients, known as prognosis. This encompasses areas such as risk prediction and predicting response to treatment. A clinical opinion piece summarises the role of prognosis in clinical care and highlights the areas where AI has already had an impact in this area. The technical section summarizes the state-of-the-art in outcome prediction, focusing on three clinical applications as exemplars: predicting response to cardiac resynchronization therapy (CRT), predicting outcome in atrial fibrillation and risk stratification in ventricular arrhythmia. A practical tutorial reinforces these concepts by taking the reader through a simple outcome prediction task based on cardiac morphology. The closing clinical opinion piece highlights areas where AI could impact prognostic tasks in the future