ArticleProceedings of SPIE--the International Society for Optical Engineering2021
Co-localization of fluorescent signals using deep learning with Manders overlapping coefficient.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Connecting HTT intermediate alleles and microRNA dysregulation to enhanced tauopathy in late-onset Alzheimer's disease.Alzheimer's research & therapy · 2026Article
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5 authors.
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Abstract
Object-based co-localization of fluorescent signals allows the assessment of interactions between two (or more) biological entities using spatial information. It relies on object identification with high accuracy to separate fluorescent signals from the background. Object detectors using convolutional neural networks (CNN) with annotated training samples could facilitate the process by detecting and counting fluorescent-labeled cells from fluorescence photomicrographs. However, datasets containing segmented annotations of colocalized cells are generally not available, and creating a new dataset with delineated masks is label-intensive. Also, the co-localization coefficient is often not used as a component during training with the CNN model. Yet, it may aid with localizing and detecting objects during training and testing. In this work, we propose to address these issues by using a quantification coefficient for co-localization called Manders overlapping coefficient (MOC)
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