Joint Alignment of Multivariate Quasi-Periodic Functional Data Using Deep Learning

The joint alignment of multivariate functional data plays an important role in various fields such as signal processing, neuroscience and medicine, including the statistical analysis of data from wearable devices. Traditional methods often ignore the phase variability and instead focus on the variability in the observed amplitude. We present a novel method for joint alignment of multivariate quasi-periodic functions using deep neural networks, decomposing, but retaining all the information in the data by preserving both phase and amplitude variability. Our proposed neural network uses a special activation of the output that builds on the unit simplex transformation, and we utilize a loss function based on the Fisher-Rao metric to train our model. Furthermore, our method is unsupervised and can provide an optimal common template function as well as subject-specific templates. We demonstrate our method on two simulated datasets and one real example, comprising data from 12-lead 10s electrocardiogram recordings.Comment: 28 pages, 6 figure

Guiding 3D U-nets with signed distance fields for creating 3D models from images

Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.Comment: MIDL 2019 [arXiv:1907.08612

Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) presenting with ventricular fibrillation in an adult: a case report

Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital anomaly. The usual clinical course is severe left sided heart failure and mitral valve insufficiency presenting during the first months of life. However, in some cases collateral blood supply from the right coronary artery is sufficient and symptoms may be subtle or even absent. Arrhythmias or sudden cardiac death in adult life may be the first clinical presentation in patients with ALCAPA. We report a case, where a 39-year old woman presented with ventricular fibrillation during phycial exertion. Coronary angiography and CT-angiography revealed an anomalous origin of the left coronary artery, and an aortic reimplantation of the left coronary artery was performed followed by ICD implantation. A review of the literature on ALCAPA is presented along with CT images before and after surgery

71-year-old woman with dizziness and lipomatous hypertrophy of the left atrium

Lipomatous hypertrophy, an unencapsulated atrial mass of adipose tissue, occurs in 1% of the population; the clinical significance of this is uncertain. Diagnosis is by echocardiography, computed tomography or magnetic resonance imaging scan. Surgical intervention is thought to be indicated in patients with obstruction, thromboembolism, uncontrollable arrhythmia or when liposarcoma cannot be excluded. We describe a case in which a 71-year-old woman was diagnosed with lipomatous hypertrophy of the left atrium. The finding of a large atrial mass was unexpected in this case. The clinical implications of the finding are unclear, since the aetiology and prognostic consequences are unknown. As the finding is not that uncommon others may find similar cases. It is therefore important that echocardiographers are aware of this entity and the aspects one needs to consider when deciding upon the best evaluation and treatment strategy