Learning to segment neurons with non-local quality measures

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 419-427

  Kroeger, Thorben ^a   Mikula, Shawn ^b   Denk, Winfried ^b   Koethe, Ullrich ^a   Hamprecht, Fred A ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b MAX PLANCK INSTITUTE FOR MEDICAL RESEARCH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ERRORS; GLOBAL SEGMENTATION; NEURAL CIRCUIT RECONSTRUCTION; PREDICTION PROBLEM; SEGMENTATION SCHEME; STRUCTURED LEARNING; UNSTRUCTURED LEARNING; VARIATION OF INFORMATIONS;

ARTIFICIAL INTELLIGENCE;

COMPUTER SCIENCE;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; ELECTRON MICROSCOPY; IMAGE ENHANCEMENT; METHODOLOGY; MOUSE; NERVE CELL; NERVE CELL NETWORK; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MICE; MICROSCOPY, ELECTRON; NERVE NET; NEURONS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84885924112     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_52     Document Type: Conference Paper

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