Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7511 LNCS, Issue , 2012, Pages 17-24

  Wang, Peng ^a   Zheng, Yefeng ^a   John, Matthias ^b   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD VESSELS; CATHETERS; E-LEARNING; MEDICAL COMPUTING; MEDICAL IMAGING; MOTION COMPENSATION; TEMPLATE MATCHING; THREE DIMENSIONAL COMPUTER GRAPHICS;

CLASSIFICATION ERRORS; CLINICAL APPLICATION; CLUTTERED BACKGROUNDS; DISCRIMINANT MODELS; IMAGE-GUIDED INTERVENTION; LINEAR DISCRIMINANTS; QUANTITATIVE EVALUATION; TRANSCATHETER AORTIC VALVES;

LEARNING SYSTEMS;

ALGORITHM; AORTA VALVE; ARTICLE; ARTIFACT; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED SURGERY; HEART CATHETERIZATION; HEART VALVE REPLACEMENT; HUMAN; METHODOLOGY; MOTION; ONLINE SYSTEM; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; AORTIC VALVE; ARTIFACTS; ARTIFICIAL INTELLIGENCE; CARDIAC CATHETERIZATION; HEART VALVE PROSTHESIS IMPLANTATION; HUMANS; MOTION; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SURGERY, COMPUTER-ASSISTED;

EID: 84872930741     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33418-4_3     Document Type: Conference Paper

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