Contour-driven regression for label inference in atlas-based segmentation

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

Volumn 8151 LNCS, Issue PART 3, 2013, Pages 211-218

  Wachinger, Christian ^a,b   Sharp, Gregory C ^b   Golland, Polina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATLAS-BASED SEGMENTATION; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; HEAD-AND-NECK CANCER; POSTERIOR DISTRIBUTIONS; PRIOR DISTRIBUTION; SEGMENTATION ACCURACY; SEGMENTATION TECHNIQUES; INFERENCE PROBLEM;

COMPUTERIZED TOMOGRAPHY; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; MAPS; MEDICAL COMPUTING; MEDICAL IMAGING; RADIOTHERAPY;

MAPS; COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HEAD AND NECK TUMOR; HUMAN; IMAGE QUALITY; METHODOLOGY; RADIOGRAPHY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; HEAD AND NECK NEOPLASMS; HUMANS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84894612114     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40760-4_27     Document Type: Conference Paper

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