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1. SEGMENTATION OF MULTIPLE HEART CAVITIES IN 3-D TRANSESOPHAGEAL
ULTRASOUND IMAGES
ABSTRACT
Three-dimensional trances ophageal echocardio graphy(TEE) is an excellent modality for
real-time visualizationof the heart and monitoring of interventions. To improve theusability of 3-
D TEE for intervention monitoring and catheterguidance, automated segmentation is desired.
However, 3-DTEE segmentation is still a challenging task due to the complexanatomy with
multiple cavities, the limited TEE field ofview, and typical ultrasound artifacts. We propose to
segmentall cavities within the TEE view with a multi-cavity activeshape model (ASM) in
conjunction with a tissue/blood classificationbased on a gamma mixture model (GMM). 3-D
TEEimage data of twenty patients were acquired with a PhilipsX7–2t matrix TEE probe. Tissue
probability maps were estimatedby a two-class (blood/tissue) GMM. A statistical shapemodel
containing the left ventricle, right ventricle, left atrium,right atrium, and aorta was derived from
computed tomographyangiography (CTA) segmentations by principal componentanalysis. ASMs
of the whole heart and individual cavities weregenerated and consecutively fitted to tissue
probability maps.First, an average whole-heart model was aligned with the 3-DTEE based on
three manually indicated anatomical landmarks.Second, pose and shape of the whole-heart ASM
were fitted bya weighted update scheme excluding parts outside of the imagesector. Third, pose
and shape of ASM for individual heartcavities were initialized by the previous whole heart ASM
andupdated in a regularized manner to fit the tissue probabilitymaps. The ASM segmentations
were validated against manualoutlines by two observers and CTA derived segmentations.