@inproceedings {Simula.simula.2708,
	title = {3D Heart Modeling With Cellular Automata, Mass-Spring System and CUDA},
	journal = {Parallel computing technologies},
	volume = {7979},
	year = {2013},
	pages = {296-309},
	publisher = {Springer},
	type = {Conference},
	abstract = {The mechanical behavior of the heart is guided by the propagation of an electrical wave, called action potential. Many diseases have multiple effects on both electrical and mechanical cardiac physiology. To support a better understanding of the multi-scale and multi-physics processes involved in physiological and pathological cardiac conditions, a lot of work has been done in developing computational tools to simulate the electro-mechanical behavior of the heart. In this work, we implemented an aplication to mimic the heart tissue behavior, based on cellular automaton, mass-spring system and parallel computing with CUDA. Our application performed 3D simulations in a very short time. In order to assess the simulation results, we compared them with another synthetic model based on well-known partial differential equations(PDE). Preliminary results suggest our application was able to reproduce the PDE results with much less computational effort.},
	author = {Ricardo Silva {Campos} and Ronan Mendonca {Amorim} and Bernardo Lino {de Oliveira} and Sundnes, Joakim and Luis Paulo da S {Barra} and Lobosco, Marcelo and Rodrigo Weber dos {Santos}},
	editor = {Malyshkin, Victor}
}
@article {Simula.simula.1442,
	title = {Automatic Segmentation of Cardiac MRI Using Snakes and Genetic Algorithms},
	journal = {Lecture Notes in Computer Science},
	volume = {5103},
	number = {1},
	year = {2008},
	pages = {168-177},
	abstract = {In this work we study and implement techniques for the automatic segmentation of cardiac Magnetic Resonance Images. The methods are based on the active contours algorithm called Snakes, which are adapted and tailored to the specific task of automatic segmentation of the left ventricle of the heart in Magnetic Resonance Images. We propose a new external force to improve the convergence of the Snakes method. In addition, a genetic algorithm is used to find the best set of configuration parameters for the Snakes method. The algorithms are implemented in Java and threads are used to explore data parallelism on shared-memory machines. Tests are performed on 150 short-axis images acquired from two healthy volunteers. Preliminary results suggest the proposed methods are promising and with further development and validation may be used, for instance, for the automatic calculation of cardiac ejection fraction.},
	author = {Gustavo Miranda {Teixeira} and Igor Ramalho {Pommeranzenbaum} and Bernardo Lino {de Oliveira} and Lobosco, Marcelo and Rodrigo Weber dos {Santos}}
}
