Patient-specific semi-supervised learning for postoperative brain tumor segmentation

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 714-721

  Meier, Raphael ^a   Bauer, Stefan ^a,b   Slotboom, Johannes ^b   Wiest, Roland ^b   Reyes, Mauricio ^a  

^a UNIVERSITY OF BERN   (Switzerland)

^b UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; MAGNETIC RESONANCE IMAGING; SUPERVISED LEARNING; MEDICAL COMPUTING; MEDICAL IMAGING; TUMORS;

BETTER PERFORMANCE; BRAIN TUMOR SEGMENTATION; COMPUTATION TIME; HIGH-GRADE GLIOMAS; PATIENT SPECIFIC; SEGMENTATION METHODS; SEGMENTATION PERFORMANCE; SEMI-SUPERVISED LEARNING; AUTOMATIC SEGMENTATIONS; SEMI- SUPERVISED LEARNING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; DIFFERENTIAL DIAGNOSIS; GLIOMA; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NEUROSURGERY; PATHOLOGY; POSTOPERATIVE CARE; POSTOPERATIVE HEMORRHAGE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

ALGORITHMS; BRAIN NEOPLASMS; DIAGNOSIS, DIFFERENTIAL; GLIOMA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEUROSURGICAL PROCEDURES; PATTERN RECOGNITION, AUTOMATED; POSTOPERATIVE CARE; POSTOPERATIVE HEMORRHAGE; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

EID: 84909599506     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10404-1_89     Document Type: Conference Paper

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