ArticlePloS one2015
Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- HeLa cell segmentation using digital image processing.Heliyon · 2024Article
- An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence.Journal of the European Academy of Dermatology and Venereology : JEADV · 2023Article
- Prevalence of ground glass nodules in preschool children: a cross-sectional study.Translational pediatrics · 2022Article
- A Novel Method for Effective Cell Segmentation and Tracking in Phase Contrast Microscopic Images.Sensors (Basel, Switzerland) · 2021Article
- Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Lack of Delta-Sarcoglycan (International journal of molecular sciences · 2019Article
- Plasmonic Bubble Nucleation and Growth in Water: Effect of Dissolved Air.The journal of physical chemistry. C, Nanomaterials and interfaces · 2019Article
- Split and Merge Watershed: a two-step method for cell segmentation in fluorescence microscopy images.Biomedical signal processing and control · 2019Article
- Disrupted alternative splicing for genes implicated in splicing and ciliogenesis causes PRPF31 retinitis pigmentosa.Nature communications · 2018Article
- A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring.Sensors (Basel, Switzerland) · 2018Article
- Automated image segmentation-assisted flattening of atomic force microscopy images.Beilstein journal of nanotechnology · 2018Article
- Large population cell characterization using quantitative phase cytometer.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis.Cellular and molecular bioengineering · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.
Indexed as
Identifiers
What OpenQuestion holds
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.