Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling.
作者： A.M. Pouch , H. Wang , M. Takabe , B.M. Jackson , J.H. Gorman
DOI: 10.1016/J.MEDIA.2013.10.001
关键词: Computer vision 、 Anatomy 、 Artificial intelligence 、 Computer science 、 Segmentation 、 Medial representation 、 Fully automatic 、 Population 、 Modality (human–computer interaction) 、 Joint (audio engineering) 、 Coordinate system 、 Mitral valve
摘要: Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis surgical treatment disease. Real-time 3D transesophageal echocardiography (3D TEE) a practical, highly informative imaging modality for examining clinical setting. To facilitate TEE image analysis, we describe fully automated method segmenting leaflets data. The algorithm integrates complementary probabilistic segmentation shape modeling techniques (multi-atlas joint label fusion deformable with continuous medial representation) automatically generate geometric models from These are unique that they establish shape-based coordinate system on valves different subjects represent volumetrically, as structures locally varying thickness. In this work, expert gold standard evaluating automatic segmentation. Without any user interaction, demonstrate accurately captures patient-specific leaflet geometry at both systole diastole data acquired mixed population normal
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