Automatic segmentation and implicit surface representation of dynamic cardiac data

Abstract

AbstractSegmentation of anatomical structures on 2D images of cardiac exams is necessary for performing 3D volumetric analysis, enabling the computation of parameters for diagnosing cardiovascular disease. In this work, we present robust algorithms to automatically segment cardiac imaging data and generate a volumetric anatomical reconstruction of a patient-specific heart model by propagating active contour output within a patient stack through a self-supervised learning model. Contour initializations are automatically generated, then output segmentations on sparse image slices are transferred and merged across a stack of images within the same heart data set during the segmentation process. We demonstrate whole-heart segmentation and compare the results with ground truth manual annotations. Additionally, we provide a framework to represent segmented heart data in the form of implicit surfaces, allowing interpolation operations to generate intermediary models of heart sections and volumes throughout the cardiac cycle and to estimate ejection fraction.