Detection of nuclei is an important step in phenotypic profiling of (a) histology sections imaged in bright field; and (b) colony formation of the 3D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition and subsequent nuclear detection using convolutional neural networks (CNN)s independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for color decomposition (CD) usings non-negative matrix factorization (NMF). Several permutations of input data representations and network architectures are evaluated to show that by coupling improved color decomposition and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular- or necrotic-phenotypes, or poor staining are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.
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