title: Automatic basal slice detection for cardiac analysis.
abstract: Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction of the left ventricle. Despite all the effort placed on automatic cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, suffers from high interobserver variability. As a result, an automatic algorithm for basal slice identification is required. Guidelines published in 2013 identify the basal slice based on the percentage of myocardium surrounding the blood cavity in the short-axis view. Existing methods, however, assume that the basal slice is the first short-axis view slice below the mitral valve and are consequently at times identifying the incorrect short-axis slice. Correct identification of the basal slice under the Society for Cardiovascular Magnetic Resonance guidelines is challenging due to the poor image quality and blood movement during image acquisition. This paper proposes an automatic tool that utilizes the two-chamber view to determine the basal slice while following the guidelines. To this end, an active shape model is trained to segment the two-chamber view and create temporal binary profiles from which the basal slice is identified. From the 51 tested cases, our method obtains 92% and 84% accurate basal slice detection for the end-systole and the end-diastole, respectively.
authors.affiliation: National University of Singapore , School of Computing, Department of Computer Science, Media Research Lab 4, AS6, Computing 1, 13 Computing Drive, 117417, Singapore.
authors.forename: Mahsa
authors.initials: M
authors.lastname: Paknezhad
authors.affiliation: ASTAR-NUS Clinical Imaging Research Centre , Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, 117599, Singapore.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Marchesseau
authors.affiliation: York University , Lassonde School of Engineering, Department of Electrical Engineering and Computer Science, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Brown
keywords: MRI; active shape model; basal slice selection; cardiac; long-axis view; two-chamber view