ArticlePloS one2015
Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.
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 9 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.
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.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models.Medicine in novel technology and devices · 2024Article
- Spatiotemporal Tracking of Different Cell Populations in Cancer Organoid Models for Investigations on Photodynamic Therapy.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets.Lab on a chip · 2020Article
- Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.Computational and structural biotechnology journal · 2020Review
- Machine learning and image-based profiling in drug discovery.Current opinion in systems biology · 2018Review
- Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids.Scientific reports · 2017Article
- A high-content image analysis approach for quantitative measurements of chemosensitivity in patient-derived tumor microtissues.Scientific reports · 2017Article
- Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.Bioinformatics (Oxford, England) · 2017Article
- Development of a 3D Tissue Culture-Based High-Content Screening Platform That Uses Phenotypic Profiling to Discriminate Selective Inhibitors of Receptor Tyrosine Kinases.Journal of biomolecular screening · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.
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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.