Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform
| العنوان: | Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform |
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| المؤلفون: | Boris Guirao, M. Durande, Hélène Delanoë-Ayari, Tess Homan, Sham Tlili, François Graner |
| المساهمون: | Matière et Systèmes Complexes (MSC (UMR_7057)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Biophysique (BIOPHYSIQUE), Institut Lumière Matière [Villeurbanne] (ILM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), National University of Singapore (NUS), Génétique et Biologie du Développement, Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | Physical Review E Physical Review E, American Physical Society (APS), 2019, 99 (6), pp.062401. ⟨10.1103/PhysRevE.99.062401⟩ Physical Review E, American Physical Society (APS), 2019, 99 (6), ⟨10.1103/PhysRevE.99.062401⟩ |
| بيانات النشر: | arXiv, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Time Factors, Image quality, Computer science, Pipeline (computing), [SDV]Life Sciences [q-bio], FOS: Physical sciences, Chick Embryo, 01 natural sciences, Epithelium, 010305 fluids & plasmas, Image (mathematics), Set (abstract data type), symbols.namesake, [SPI]Engineering Sciences [physics], 0103 physical sciences, Cell Behavior (q-bio.CB), Animals, [CHIM]Chemical Sciences, Segmentation, Cell migration, [PHYS.COND]Physics [physics]/Condensed Matter [cond-mat], 010306 general physics, Anisotropy, Tissues and Organs (q-bio.TO), Cell Shape, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Cell Size, [PHYS]Physics [physics], Fourier Analysis, business.industry, Pupa, Pattern recognition, Quantitative Biology - Tissues and Organs, Cell mechanics, Physics - Medical Physics, Biomechanical Phenomena, Molecular Imaging, Fourier transform, Drosophila melanogaster, Fourier analysis, FOS: Biological sciences, symbols, Quantitative Biology - Cell Behavior, Artificial intelligence, Stress, Mechanical, Medical Physics (physics.med-ph), business, Rheology |
| الوصف: | Mechanical strain and stress play a major role in biological processes such as wound healing or morphogenesis. To assess this role quantitatively, fixed or live images of tissues are acquired at a cellular precision in large fields of views. To exploit these data, large numbers of cells have to be analyzed to extract cell shape anisotropy and cell size. Most frequently, this is performed through detailed individual cell contour determination, using so-called segmentation computer programs, complemented if necessary by manual detection and error corrections. However, a coarse grained and faster technique can be recommended in at least three situations. First, when detailed information on individual cell contours is not required, for instance in studies which require only coarse-grained average information on cell anisotropy. Second, as an exploratory step to determine whether full segmentation can be potentially useful. Third, when segmentation is too difficult, for instance due to poor image quality or too large a cell number. We developed a user-friendly, Fourier transform-based image analysis pipeline. It is fast (typically $10^4$ cells per minute with a current laptop computer) and suitable for time, space or ensemble averages. We validate it on one set of artificial images and on two sets of fully segmented images, one from a Drosophila pupa and the other from a chicken embryo; the pipeline results are robust. Perspectives include \textit{in vitro} tissues, non-biological cellular patterns such as foams, and $xyz$ stacks. Comment: 13 pages; 9 figures |
| تدمد: | 2470-0045 2470-0053 |
| DOI: | 10.48550/arxiv.1810.11652 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae2e910a6ff04f6198ee92ab8966214b |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....ae2e910a6ff04f6198ee92ab8966214b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24700045 24700053 |
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| DOI: | 10.48550/arxiv.1810.11652 |