Fast segmentation of the left ventricle in cardiac MRI using dynamic programming.

BACKGROUND AND OBJECTIVE: The segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging is a necessary step for the analysis and diagnosis of cardiac function. In most clinical setups, this step is still manually performed by cardiologists, which is time-consuming and laborious. This paper proposes a fast system for the segmentation of the LV that significantly reduces human intervention.

METHODS: A dynamic programming approach is used to obtain the border of the LV. Using very simple assumptions about the expected shape and location of the segmentation, this system is able to deal with many of the challenges associated with this problem. The system was evaluated on two public datasets: one with 33 patients, comprising a total of 660 magnetic resonance volumes and another with 45 patients, comprising a total of 90 volumes. Quantitative evaluation of the segmentation accuracy and computational complexity was performed.

RESULTS: The proposed system is able to segment a whole volume in 1.5 seconds and achieves an average Dice similarity coefficient of 86.0% and an average perpendicular distance of 2.4 mm, which compares favorably with other state-of-the-art methods.

CONCLUSIONS: A system for the segmentation of the left ventricle in cardiac magnetic resonance imaging is proposed. It is a fast framework that significantly reduces the amount of time and work required of cardiologists.