ArticleComputers in biology and medicine2016
Rapid 3-D delineation of cell nuclei for high-content screening platforms.
Article in Computers in biology and medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Human Y chromosome pan-organ mapping reveals progressive mosaic loss from normal to cancer.JCI insight · 2026Article
- High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos.Nature communications · 2023Article
- Directing iPSC differentiation into iTenocytes using combined scleraxis overexpression and cyclic loading.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2023Article
- A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture.Biophysics reviews · 2022Review
- Efficient automatic 3D segmentation of cell nuclei for high-content screening.BMC bioinformatics · 2022Article
- Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images of Mouse Embryos Using Convolutional Neural Networks.IEEE access : practical innovations, open solutions · 2022Article
- Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network.IEEE access : practical innovations, open solutions · 2021Article
- Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations.Computational and mathematical methods in medicine · 2021Article
- CytoCensus, mapping cell identity and division in tissues and organs using machine learning.eLife · 2020Article
- Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm.Journal of digital imaging · 2020Article
- DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.Scientific reports · 2019Article
- Advances in the computational and molecular understanding of the prostate cancer cell nucleus.Journal of cellular biochemistry · 2018Review
- CAS: Cell Annotation Software - Research on Neuronal Tissue Has Never Been so Transparent.Neuroinformatics · 2017Article
- A novel toolbox to investigate tissue spatial organization applied to the study of the islets of Langerhans.Scientific reports · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
High-resolution three-dimensional (3-D) microscopy combined with multiplexing of fluorescent labels allows high-content analysis of large numbers of cell nuclei. The full automation of 3-D screening platforms necessitates image processing algorithms that can accurately and robustly delineate nuclei in images with little to no human intervention. Imaging-based high-content screening was originally developed as a powerful tool for drug discovery. However, cell confluency, complexity of nuclear staining as well as poor contrast between nuclei and background result in slow and unreliable 3-D image processing and therefore negatively affect the performance of studying a drug response. Here, we propose a new method, 3D-RSD, to delineate nuclei by means of 3-D radial symmetries and test it on high-resolution image data of human cancer cells treated by drugs. The nuclei detection performance was evaluated by means of manually generated ground truth from 2351 nuclei (27 confocal stacks). When compared to three other nuclei segmentation methods, 3D-RSD possessed a better true positive rate of 83.3% and F-score of 0.895±0.045 (p-value=0.047). Altogether, 3D-RSD is a method with a very good overall segmentation performance. Furthermore, implementation of radial symmetries offers good processing speed, and makes 3D-RSD less sensitive to staining patterns. In particular, the 3D-RSD method performs well in cell lines, which are often used in imaging-based HCS platforms and are afflicted by nuclear crowding and overlaps that hinder feature extraction.
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