Combining generative models for multifocal glioma segmentation and registration

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

Volumn 8673 LNCS, Issue PART 1, 2014, Pages 763-770

  Kwon, Dongjin ^a   Shinohara, Russell T ^a   Akbari, Hamed ^a   Davatzikos, Christos ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RETRIEVAL; IMAGE SEGMENTATION; MEDICAL COMPUTING; MEDICAL IMAGING;

COMPLEX SHAPES; FOREGROUND INFORMATION; GENERATIVE MODEL; JOINT SEGMENTATION; MULTIPLE SEEDS; RANDOM WALK WITH RESTART; SEGMENTATION PERFORMANCE; TUMOR GROWTH MODELING;

TUMORS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN TUMOR; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; GLIOMA; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN NEOPLASMS; COMPUTER SIMULATION; GLIOMA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84909638666     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10404-1_95     Document Type: Conference Paper

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