High-content analysis in monastrol suppressor screens. A neural network-based classification approach.
OBJECTIVE
High-content screening (HCS) via automated fluorescent microscopy is a powerful technology for the effective expression of cellular processes. However, HCS will generally produce tremendous image datasets, which leads to difficulties of handling and analyzing. We proposed an automatic classification approach for simultaneous feature extraction and cell phenotype recognition of monoaster and bipolar cells in HCS system.


METHODS
The proposed approach was composed of image segmentation, feature extraction, and classification. The image segmentation was based on the Laplacian of Gaussian (LoG) edge detection method. For the reduction of noise effect on cellular images, we employed an adaptive threshold in microtubule channel. The principal component analysis was used in the feature selection process. The classification was performed with a back-propagation neural network (BPNN). Using the current approach, the cell phases were distinguished from three-channel acquisitions of cellular images and the numbers of bipolar and monoaster cells were automatically counted.


RESULTS
The validity of this approach was examined by the application of screening the response of drug compounds in suppressing Monastrol. Our results indicate that the proposed algorithm could improve the recognition rates of monoaster and bipolar cells to 97.98% and 93.12%, respectively, compared with 97.02% and 86.96% obtained from the same samples by multi-phenotypic mitotic analysis (MMA).


CONCLUSIONS
We have shown that BPNN is a valuable tool to classify cell phenotype. To further improve the classification performance, more test data, more optimized feature selection approaches, and advanced classifier may be required and will be investigated in future works.
Zhang
Z
Z
Institute of Acoustics, Key Lab of Modern Acoustics, MOE, Nanjing University, Nanjing, China.
Ge
Y
Y
Zhang
D
D
Zhou
X
X
D000465:Algorithms; D002477:Cells / Q000145:classification; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008938:Mitosis / Q000187:drug effects; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D011743:Pyrimidines / Q000494:pharmacology*; D013871:Thiones / Q000494:pharmacology*
D011743:Pyrimidines; D013871:Thiones; C400223:monastrol