3 - Learning cardiac anatomy

Abstract

An essential prerequisite for constructing the heart model is fast and robust parsing of the cardiovascular anatomy based on image data. This entails the detection, segmentation and tracking of anatomical structures or pathologies in the human heart and vascular system. Current solutions for these problems are based on machine learning and require large annotated image databases for effective training. In practice, however, these techniques often suffer from inherent limitations related to the efficiency in scanning high-dimensional parametric spaces and the learning of representative features for describing the image content. In this chapter, we present several established techniques for cardiac image parsing and structure tracking. For image parsing, we describe the marginal space learning framework, including the original version of the system that relies on handcrafted steerable features, as well as the modern redesign of the framework based on the latest automatic feature learning technology using deep learning. To address the limitations of these techniques that rely on exhaustive search, we present the concept of intelligent image parsing. Based on deep reinforcement learning and scale-space theory, this approach enables the efficient parsing of high-resolution volumetric data in real-time. Several experiments are included to analyze the performance of these methods on different problems using large datasets. This chapter also briefly describes a modern deep image-to-image neural network architecture for whole heart isolation. For cardiac structure tracking, a comprehensive review is presented of state-of-the-art structure tracking methods based on convolutional neural networks and recurrent neural networks.