ArticleIEEE transactions on medical imaging2018
Automated Cell Segmentation for Quantitative Phase Microscopy.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 33 citations in OpenAlex.
- Advancing in vitro cell migration studies: a review of open-source analytical platforms for cancer and wound healing research.Cell adhesion & migration · 2025Review
- CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.Journal of biomedical optics · 2024Article
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.iScience · 2022Article
- GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices.Journal of imaging · 2022Article
- Loss of KAP3 decreases intercellular adhesion and impairs intracellular transport of laminin in signet ring cell carcinoma of the stomach.Scientific reports · 2022Article
- Cellular analysis using label-free parallel array microscopy with Fourier ptychography.Biomedical optics express · 2022Article
- Biophysical phenotyping of mesenchymal stem cells along the osteogenic differentiation pathway.Cell biology and toxicology · 2021Review
- Self-supervised pretraining for transferable quantitative phase image cell segmentation.Biomedical optics express · 2021Article
- Practical application of microsphere samples for benchmarking a quantitative phase imaging system.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2021Article
- Acoustofluidic phase microscopy in a tilted segmentation-free configuration.Biomicrofluidics · 2021Article
- Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison.BMC bioinformatics · 2019Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.