Combining generative and discriminative models for semantic segmentation of CT scans via active learning

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

Volumn 6801 LNCS, Issue , 2011, Pages 25-36

  Iglesias, Juan Eugenio ^a,b   Konukoglu, Ender ^b   Montillo, Albert ^b,c   Tu, Zhuowen ^a   Criminisi, Antonio ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH   (United Kingdom)

^c GE GLOBAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3-D IMAGE; ACTIVE LEARNING; ANATOMICAL STRUCTURES; BODY SHAPES; COMPUTED TOMOGRAPHY; DISCRIMINATIVE MODELS; GENERATIVE MODEL; HIGH VARIABILITY; JENSEN-SHANNON DIVERGENCE; MARKOV RANDOM FIELDS; MINIMUM SUBSETS; OPTIMAL SELECTION; OPTIMAL TRAINING; PROBABILISTIC MODELS; QUANTITATIVE EVALUATION; SEGMENTATION ACCURACY; SEMANTIC SEGMENTATION; SUPERVISED CLASSIFIERS; TRAINING DATA; TRAINING IMAGE;

DATA PROCESSING; IMAGE SEGMENTATION; MEDICAL IMAGING; OPTIMIZATION; SEMANTICS; THREE DIMENSIONAL; TOMOGRAPHY;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DISCRIMINANT ANALYSIS; HUMAN; IMAGE QUALITY; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; HUMANS; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 80052312929     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22092-0_3     Document Type: Conference Paper

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