ArticleIEEE transactions on bio-medical engineering2018
Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload.Chemical & biomedical imaging · 2026Article
- 3D-Organoid-SwinNet: High-Content Profiling of 3D Organoids.IEEE journal of biomedical and health informatics · 2025Article
- Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.The American journal of pathology · 2023Article
- Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.IEEE transactions on bio-medical engineering · 2021Article
- Developing image analysis pipelines of whole-slide images: Pre- and post-processing.Journal of clinical and translational science · 2020Review
- Towards pixel-to-pixel deep nucleus detection in microscopy images.BMC bioinformatics · 2019Article
- Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.BMC bioinformatics · 2018Article
- Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
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2 authors.
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Abstract
Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks 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 CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD 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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