Automatic Ki-67 counting using robust cell detection and online dictionary learning

IEEE Transactions on Biomedical Engineering

Volumn 61, Issue 3, 2014, Pages 859-870

  Xing, Fuyong ^a   Su, Hai ^a   Neltner, Janna ^a   Yang, Lin ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Cell detection; classification; Ki 67; neuroendocrine tumor (NET); segmentation

Indexed keywords

BOUNDARY DELINEATION; CELL DETECTION; GASTROINTESTINAL TRACT; KI-67; NEUROENDOCRINE TUMORS; ONLINE DICTIONARY LEARNING; PROLIFERATION INDEX; SPARSE DICTIONARIES;

CELLS; CLASSIFICATION (OF INFORMATION); E-LEARNING; IMAGE SEGMENTATION; TUMORS;

CYTOLOGY;

KI 67 ANTIGEN;

ANALYTIC METHOD; ANTIGEN DETECTION; ARTICLE; AUTOMATION; CANCER TISSUE; CELL COUNTING; CELL PROLIFERATION; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DIAGNOSTIC TEST ACCURACY STUDY; HISTOPATHOLOGY; HUMAN; HUMAN CELL; HUMAN TISSUE; KI-67 COUNTING; LABORATORY AUTOMATION; LEARNING ALGORITHM; MEASUREMENT ACCURACY; NEUROENDOCRINE TUMOR; ONLINE DICTIONARY LEARNING; ONLINE SYSTEM; TUMOR CELL; TUMOR DIFFERENTIATION; TUMOR LOCALIZATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KI-67 ANTIGEN; NEUROENDOCRINE TUMORS; ROC CURVE;

EID: 84896840603     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2291703     Document Type: Article

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