Microscopy cell counting and detection with fully convolutional regression networks

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Volumn 6, Issue 3, 2018, Pages 283-292

  Xie, Weidi ^a   Noble, J Alison ^a   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

cell counting; cell detection; fully convolutional regression networks; inverting feature representations; Microscopy image analysis

Indexed keywords

BENCHMARKING; CELLS; CONVOLUTION; CYTOLOGY; IMAGE ENHANCEMENT; NEURAL NETWORKS;

CELL COUNTING; CELL DETECTION; DENSITY MAPS; FEATURE REPRESENTATION; FULLY CONVOLUTIONAL REGRESSION NETWORK; INPUT IMAGE; INVERTING FEATURE REPRESENTATION; MICROSCOPY IMAGE ANALYSIS; MICROSCOPY IMAGES; SYNTHETIC DATA;

REGRESSION ANALYSIS;

ALGORITHM; ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELL COUNTING; CONVOLUTIONAL REGRESSION NETWORK; EMBRYONIC STEM CELL; FIELD SIZE; FLUORESCENCE MICROSCOPY; HUMAN; IMAGE ANALYSIS; LYMPHOCYTE; MATHEMATICAL COMPUTING; PATCH SIZE; PLASMA CELL; PRIORITY JOURNAL; RETINAL PIGMENT EPITHELIUM; T CELL LYMPHOBLASTIC LEUKEMIA CELL; VALIDATION PROCESS;

EID: 85006215434     PISSN: 21681163     EISSN: 21681171     Source Type: Journal    
DOI: 10.1080/21681163.2016.1149104     Document Type: Article

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