Neural net-based identification of cells expressing the p300 tumor-related antigen using fluorescence image analysis.
We report on preliminary investigations of the use of an image analysis system to perform preliminary algorithmic classification of images of fluorochrome-labeled cells followed by capture of gray-level images of potentially abnormal cells for analysis by a neural network. Cells were labeled with an antibody against a bladder cancer tumor-associated antigen, and the neural net was used to distinguish true-positive cells from negative cells, false-positive cells (autofluorescent or nonspecific labeling), and cell-sized artifacts. Gray-level cell images were digitized and processed for analysis by a feed-forward neural network using back-propagation. The network was trained and tested with two independent image sets. Various network configurations and activation functions were investigated, including a sinusoidal activation function. At high power, the network agreed completely with the human observer's classification. At low power, a strong clustering of cells classified by the network with expert classification was seen, while the neural network showed roughly 75% concordance with the human observer. In addition, a set of four features extracted from raw cell images were investigated. The features were: shape factor, texture, area, and average pixel intensity. A network trained with these features performed better than one operating with gray-level images. We conclude that using neural networks to recognize and classify images captured by an image analysis microscope is feasible.
Hurst
R E
RE
Department of Urology, University of Oklahoma Health Sciences Center, Oklahoma City 73190, USA. robert-hurst@uokhsc.edu
Bonner
R B
RB
Ashenayi
K
K
Veltri
R W
RW
Hemstreet
G P
GP
D000951:Antigens, Neoplasm / Q000032:analysis*; D014408:Biomarkers, Tumor / Q000032:analysis*; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods; D016571:Neural Networks, Computer; D001749:Urinary Bladder Neoplasms / Q000737:chemistry / Q000175:diagnosis*
D000951:Antigens, Neoplasm; D014408:Biomarkers, Tumor