Dr. Stephanie Marchesseau Profile

Dr. Stephanie Marchesseau

at A*STAR-NUS

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Publications (2)

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SPIE Journal Paper | 20 September 2016

Automatic basal slice detection for cardiac analysis

Mahsa Paknezhad, Stephanie Marchesseau, Michael Brown

JMI, Vol. 3, Issue 03, 034004, (September 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.034004

KEYWORDS: Image segmentation, Heart, Blood, Binary data, Magnetic resonance imaging, 3D modeling, Image processing algorithms and systems, Cardiovascular magnetic resonance imaging, Data modeling, Medical imaging

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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.

Proceedings Article | 21 March 2016 Paper

Automatic basal slice detection for cardiac analysis

Mahsa Paknezhad, Stephanie Marchesseau, Michael Brown

Proceedings Volume 9784, 97842A (2016) https://doi.org/10.1117/12.2216460

KEYWORDS: Image segmentation, Image analysis, Magnetic resonance imaging, Cardiac imaging, Automatic tracking, Heart, Binary data, Cardiovascular magnetic resonance imaging, 3D modeling, Data modeling

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Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction (EF) of the left ventricle (LV). Despite research on cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, has been shown to have high inter-observer variability, with a variation of the EF by up to 8%. Therefore, an automatic way of identifying the basal slice is still required. Prior published methods operate by automatically tracking the mitral valve points from the long-axis view of the LV. These approaches assumed that the basal slice is the first short-axis slice below the mitral valve. However, guidelines published in 2013 by the society for cardiovascular magnetic resonance indicate that the basal slice is the uppermost short-axis slice with more than 50% myocardium surrounding the blood cavity. Consequently, these existing methods are at times identifying the incorrect short-axis slice. Correct identification of the basal slice under these guidelines is challenging due to the poor image quality and blood movement during image acquisition. This paper proposes an automatic tool that focuses on the two-chamber slice to find the basal slice. To this end, an active shape model is trained to automatically segment the two-chamber view for 51 samples using the leave-one-out strategy. The basal slice was detected using temporal binary profiles created for each short-axis slice from the segmented two-chamber slice. From the 51 successfully tested samples, 92% and 84% of detection results were accurate at the end-systolic and the end-diastolic phases of the cardiac cycle, respectively.

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