Modeling 3D cardiac contraction and relaxation with point cloud deformation networks

Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation process and hence, limit the understanding of both healthy and pathological cardiac mechanics. In this work, we propose the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach to model 3D cardiac contraction and relaxation between the extreme ends of the cardiac cycle. It employs the recent advances in point cloud-based deep learning into an encoder-decoder structure, in order to enable efficient multi-scale feature learning directly on multi-class 3D point cloud representations of the cardiac anatomy. We evaluate our approach on a large dataset of over 10,000 cases from the UK Biobank study and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. Furthermore, we observe similar clinical metrics between predicted and ground truth populations and show that the PCD-Net can successfully capture subpopulation-specific differences between normal subjects and myocardial infarction (MI) patients. We then demonstrate that the learned 3D deformation patterns outperform multiple clinical benchmarks by 13% and 7% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis

Multi-objective point cloud autoencoders for explainable myocardial infarction prediction

Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture more complex patterns in the heart's 3D anatomy and thus limit diagnostic accuracy. In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function. Its architecture consists of multiple task-specific branches connected by a low-dimensional latent space to allow for effective multi-objective learning of both reconstruction and MI prediction, while capturing pathology-specific 3D shape information in an interpretable latent space. Furthermore, its hierarchical branch design with point cloud-based deep learning operations enables efficient multi-scale feature learning directly on high-resolution anatomy point clouds. In our experiments on a large UK Biobank dataset, the multi-objective point cloud autoencoder is able to accurately reconstruct multi-temporal 3D shapes with Chamfer distances between predicted and input anatomies below the underlying images' pixel resolution. Our method outperforms multiple machine learning and deep learning benchmarks for the task of incident MI prediction by 19% in terms of Area Under the Receiver Operating Characteristic curve. In addition, its task-specific compact latent space exhibits easily separable control and MI clusters with clinically plausible associations between subject encodings and corresponding 3D shapes, thus demonstrating the explainability of the prediction

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions

3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ~13% and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.Comment: Accepted at EMBC 202

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

One health, une seule santé

One Health, « Une seule santé », est une stratégie mondiale visant à développer les collaborations interdisciplinaires pour la santé humaine, animale et environnementale. Elle promeut une approche intégrée, systémique et unifiée de la santé aux échelles locale, nationale et mondiale, afin de mieux affronter les maladies émergentes à risque pandémique, mais aussi s'adapter aux impacts environnementaux présents et futurs. Bien que ce mouvement s’étende, la littérature en français reste rare. Traduit de l’anglais, coordonné par d’éminents épidémiologistes et s'appuyant sur un large panel d' approches scientifiques rarement réunies autour de la santé, cet ouvrage retrace les origines du concept et présente un contenu pratique sur les outils méthodologiques, la collecte de données, les techniques de surveillance et les plans d’étude. Il combine recherche et pratique en un seul volume et constitue un ouvrage de référence unique pour la santé mondiale

Acoustic orientation in the dark: About how the brain processes naturalistic echolocation sequences in the fruit-eating bat "Carollia perspicillata"

Echolocation allows bats to orientate in darkness without using visual information. Bats emit spatially directed high frequency calls and infer spatial information from echoes coming from call reflections in objects (Simmons 2012; Moss and Surlykke 2001, 2010). The echoes provide momentary snapshots, which have to be integrated to create an acoustic image of the surroundings. The spatial resolution of the computed image increases with the quantity of received echoes. Thus, a high call rate is required for a detailed representation of the surroundings. One important parameter that the bats extract from the echoes is an object’s distance. The distance is inferred from the echo delay, which represents the duration between call emission and echo arrival (Kössl et al. 2014). The echo delay decreases with decreasing distance and delay-tuned neurons have been characterized in the ascending auditory pathway, which runs from the inferior colliculus (Wenstrup et al. 2012; Macías et al. 2016; Wenstrup and Portfors 2011; Dear and Suga 1995) to the auditory cortex (Hagemann et al. 2010; Suga and O'Neill 1979; O'Neill and Suga 1982). Electrophysiological studies usually characterize neuronal processing by using artificial and simplified versions of the echolocation signals as stimuli (Hagemann et al. 2010; Hagemann et al. 2011; Hechavarría and Kössl 2014; Hechavarría et al. 2013). The high controllability of artificial stimuli simplifies the inference of the neuronal mechanisms underlying distance processing. But, it remains largely unexplored how the neurons process delay information from echolocation sequences. The main purpose of the thesis is to investigate how natural echolocation sequences are processed in the brain of the bat Carollia perspicillata. Bats actively control the sensory information that it gathers during echolocation. This allows experimenters to easily identify and record the acoustic stimuli that are behaviorally relevant for orientation. For recording echolocation sequences, a bat was placed in the mass of a swinging pendulum (Kobler et al. 1985; Beetz et al. 2016b). During the swing the bat emitted echolocation calls that were reflected in surrounding objects. An ultrasound sensitive microphone traveling with the bat and positioned above the bat’s head recorded the echolocation sequence. The echolocation sequence carried delay information of an approach flight and was used as stimulus for neuronal recordings from the auditory cortex and inferior colliculus of the bats. Presentation of high stimulus rates to other species, such as rats, guinea pigs, suppresses cortical neuron activity (Wehr and Zador 2005; Creutzfeldt et al. 1980). Therefore, I tested if neurons of bats are suppressed when they are stimulated with high acoustic rates represented in echolocation sequences (sequence situation). Additionally, the bats were stimulated with randomized call echo elements of the sequence and an interstimulus time interval of 400 ms (element situation). To quantify neuronal suppression induced by the sequence, I compared the response pattern to the sequence situation with the concatenated response patterns to the element situation. Surprisingly, although the bats should be adapted for processing high acoustic rates, their cortical neurons are vastly suppressed in the sequence situation (Beetz et al. 2016b). However, instead of being completely suppressed during the sequence situation, the neurons partially recover from suppression at a unit specific call echo element. Multi-electrode recordings from the cortex allow assessment of the representation of echo delays along the cortical surface. At the cortical level, delay-tuned neurons are topographically organized. Cortical suppression improves sharpness of neuronal tuning and decreases the blurriness of the topographic map. With neuronal recordings from the inferior colliculus, I tested whether the echolocation sequence also induced neuronal suppression at subcortical level. The sequence induced suppression was weaker in the inferior colliculus than in the cortex. The collicular response makes the neurons able to track the acoustic events in the echolocation sequence. Collicular suppression mainly improves the signal-to-noise ratio. In conclusion, the results demonstrate that cortical suppression is not necessarily a shortcoming for temporal processing of rapidly occurring stimuli as it has previously been interpreted. Natural environments are usually composed of multiple objects. Thus, each echolocation call reflects off multiple objects resulting in multiple echoes following the calls. At present, it is largely unexplored how neurons process echolocation sequences containing echo information from more than one object (multi-object sequences). Therefore, I stimulated bats with a multi-object sequence which contained echo information from three objects. The objects were different distances away from each other. I tested the influence of each object on the neuronal tuning by stimulating the bats with different sequences created from filtering object specific echoes from the multi-object sequence. The cortex most reliably processes echo information from the nearest object whereas echo information from distant objects is not processed due to neuronal suppression. Collicular neurons process less selectively echo information from certain objects and respond to each echo. For proper echolocation, bats have to distinguish between own biosonar signals and the signals coming from conspecifics. This can be quite challenging when many bats echolocate adjacent to each other. In behavioral experiments, the echolocation performance of C. perspicillata was tested in the presence of potentially interfering sounds. In the presence of acoustic noise, the bats increase the sensory acquisition rate which may increase the update rate of sensory processing. Neuronal recordings from the auditory cortex and inferior colliculus could strengthen the hypothesis. Although there were signs of acoustic interference or jamming at neuronal level, the neurons were not completely suppressed and responded to the rest of the echolocation sequence

Multi-domain variational autoencoders for combined modeling of MRI-based biventricular anatomy and ECG-based cardiac electrophysiology

Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach

Neural processing of naturalistic echolocation signals in bats

Echolocation behavior, a navigation strategy based on acoustic signals, allows scientists to explore neural processing of behaviorally relevant stimuli. For the purpose of orientation, bats broadcast echolocation calls and extract spatial information from the echoes. Because bats control call emission and thus the availability of spatial information, the behavioral relevance of these signals is undiscussable. While most neurophysiological studies, conducted in the past, used synthesized acoustic stimuli that mimic portions of the echolocation signals, recent progress has been made to understand how naturalistic echolocation signals are encoded in the bat brain. Here, we review how does stimulus history affect neural processing, how spatial information from multiple objects and how echolocation signals embedded in a naturalistic, noisy environment are processed in the bat brain. We end our review by discussing the huge potential that state-of-the-art recording techniques provide to gain a more complete picture on the neuroethology of echolocation behavior

Combined generation of electrocardiogram and cardiac anatomy models using multi-modal variational autoencoders

Understanding population-wide variability of the human heart is crucial to detect abnormalities and improve the assessment of both cardiac anatomy and function. While many computational modeling approaches have been developed to capture this variability separately for either cardiac anatomy or physiology, their complex interconnections have rarely been explored together. In this work, we propose a novel multi-modal variational autoencoder (VAE) capable of processing combined physiology and bitemporal anatomy information in the form of electrocardiograms (ECG) and 3D biventricular point clouds. Our method achieves high reconstruction accuracy on a UK Biobank dataset with Chamfer distances between predicted and input anatomies below the underlying image resolution and the ECG reconstructions outperforming a state-of-the-art benchmark approach specialized in ECG generation. We also evaluate its generative ability and find comparable populations of generated and gold standard anatomies, ECGs, and combined anatomy-ECG data in terms of common clinical metrics and maximum mean discrepancies