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Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces
Katholieke University of Leuven, Belgium.
Katholieke University of Leuven, Belgium; University of Minho, Portugal; University of Minho, Portugal.
University of Lyon 1, France.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
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2017 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 36, no 11, p. 2287-2296Article in journal (Refereed) Published
Abstract [en]

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 36, no 11, p. 2287-2296
Keywords [en]
3-D echocardiography; left ventricle segmentation; B-spline explicit active surfaces; statistical shape model; localized anatomical affine optical flow
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-142967DOI: 10.1109/TMI.2017.2734959ISI: 000414134200009PubMedID: 28783626OAI: oai:DiVA.org:liu-142967DiVA, id: diva2:1156568
Note

Funding Agencies|European Research Council under the European Union [281748]; FCT Fundacao para a Ciencia e a Tecnologia, Portugal [SFRH/BD/93443/2013]

Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2018-01-13

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Engvall, Jan
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Division of Cardiovascular MedicineFaculty of Medicine and Health SciencesDepartment of Clinical Physiology in Linköping
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