Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/45071

TitleAortic valve tract segmentation from 3D-TEE using shape-based B-spline explicit active surfaces
Author(s)Queirós, Sandro Filipe Monteiro
Papachristidis, Alexandros
Barbosa, Daniel Joaquim Cunha
Theodoropoulos, Konstantinos C.
Fonseca, Jaime C.
Monaghan, Mark J.
Vilaça, João L.
D'hooge, Jan
KeywordsImage segmentation
Valves
Shape
Splines (mathematics)
Three-dimensional displays
Surface morphology
Imaging
3D transesophageal echocardiography
aortic valve segmentation
B-spline explicit active surfaces
profile-based statistical shape model
Issue dateMar-2016
PublisherIEEE
JournalIEEE Transactions on Medical Imaging
CitationQueirós, S., Papachristidis, A., Barbosa, D., Theodoropoulos, K. C., Fonseca, J. C., Monaghan, M. J., et. al.(2016). Aortic valve tract segmentation from 3D-TEE using shape-based B-spline Explicit Active Surfaces. IEEE transactions on medical imaging, 35(9), 2015-2025
Abstract(s)A novel semi-automatic algorithm for aortic valve (AV) wall segmentation is presented for 3D transesophageal echocardiography (TEE) datasets. The proposed methodology uses a 3D cylindrical formulation of the B-spline Explicit Active Surfaces (BEAS) framework in a dual-stage energy evolution process, comprising a threshold-based and a localized region-based stage. Hereto, intensity and shape-based features are combined to accurately delineate the AV wall from the ascending aorta (AA) to the left ventricular outflow tract (LVOT). Shape-prior information is included using a profile-based statistical shape model (SSM), and embedded in BEAS through two novel regularization terms: one confining the segmented AV profiles to shapes seen in the SSM (hard regularization) and another penalizing according to the profile's degree of likelihood (soft regularization). The proposed energy functional takes thus advantage of the intensity data in regions with strong image content, while complementing it with shape knowledge in regions with nearly absent image data. The proposed algorithm has been validated in 20 3D-TEE datasets with both stenotic and non-stenotic valves. It was shown to be accurate, robust and computationally efficient, taking less than 1 second to segment the AV wall from the AA to the LVOT with an average accuracy of 0.78 mm. Semi-automatically extracted measurements at four relevant anatomical levels (LVOT, aortic annulus, sinuses of Valsalva and sinotubular junction) showed an excellent agreement with experts' ones, with a higher reproducibility than manually-extracted measures.
TypeArticle
URIhttps://hdl.handle.net/1822/45071
DOI10.1109/TMI.2016.2544199
ISSN0278-0062
e-ISSN1558-254X
Publisher versionhttp://ieeexplore.ieee.org/document/7436779/
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ICVS - Artigos em revistas internacionais / Papers in international journals
DEI - Artigos em revistas internacionais

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