Segmentation of three-dimensional (3D) transesophageal ultrasound (TEE) is highly desired for intervention monitoring and guidance, but it is still a challenging image processing task due to complex local anatomy, limited field of view and typical ultrasound artifacts. We propose to use a multi-cavity active shape model (ASM) derived from Computed Tomography Angiography (CTA) segmentations in conjunction with a blood/tissue classification by Gamma Mixture Models to identify and segment the individual cavities simultaneously. A scheme that utilized successively ASMs of the whole heart and the individual cavities was used to segment the entire heart. We successfully validated our segmentation scheme with manually outlined contours and with CTA segmentations for three patients. The segmentations of the three patients had an average distance of 2.3, 4.9, and 2.1 mm to the manual outlines. © 2013 Springer-Verlag.
|Naam||Lecture Notes in Computer Science|
|ISSN van geprinte versie||0302-9743|
|Workshop||6th International Workshop on Medical Imaging and Augmented Reality (MIAR 2013)/ 8th International Workshop on Augmented Environments for Computer-Assisted Interventions (AE-CAI 2013)|
|Verkorte titel||MIAR/AE-CAI 2013|
|Periode||22/09/13 → 22/09/13|