Chapter 14 Segmentation and Blood Flow Simulations of Patient-Specific Heart Data Dimitris Metaxas, Scott Kulp, Mingchen Gao, Shaoting Zhang, Zhen Qian, and Leon Axel Abstract In this chapter, we present a fully automatic and accurate segmentation framework for 2D cardiac tagged MR images, a semiautomatic method for 3D segmentation from CT data, and the results of blood flow simulation using these highly detailed models. The 2D segmentation system consists of a semiautomatic segmentation framework to obtain the training contours, and a learning-based framework that is trained by the semiautomatic results, and achieves fully automatic and accurate segmentation. We then present a method to simulate and visualize blood flow through the human heart, using the reconstructed 4D motion of the endocardial surface of the left ventricle as boundary conditions. The reconstruction captures the motion of the full 3D surfaces of the complex features, such as the papillary muscles and the ventricular trabeculae. We use visualizations of the flow field to view the interactions between the blood and the trabeculae in far more detail than has been achieved previously, which promises to give a better understanding of cardiac flow. Finally, we use our simulation results to compare the blood flow within one healthy heart and two diseased hearts. D. Metaxas () • S. Kulp • M. Gao • S. Zhang CBIM, Rutgers University, New Brunswick, NJ, USA e-mail: dnm@cs.rutgers.edu; sckulp@cs.rutgers.edu; minggao@cs.rutgers.edu; shaoting@cs.rutgers.edu Z. Qian Piedmont Heart Institute, Atlanta, GA, USA e-mail: Zhen.Qian@piedmont.org L. Axel NYU School of Medicine, New York, NY, USA e-mail: Leon.Axel@nyumc.org M. Garbey et al. (eds.), Computational Surgery and Dual Training: Computing, Robotics and Imaging, DOI 10.1007/978-1-4614-8648-0__14, © Springer Science+Business Media New York 2014 213