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Artykuły w czasopismach na temat "Embryo segmentation"
Sheeba, Caroline J. "Mechanisms of vertebrate embryo segmentation". Seminars in Cell & Developmental Biology 49 (styczeń 2016): 57–58. http://dx.doi.org/10.1016/j.semcdb.2016.01.041.
Pełny tekst źródłaKornberg, Thomas B., i Tetsuya Tabata. "Segmentation of the Drosophila embryo". Current Opinion in Genetics & Development 3, nr 4 (styczeń 1993): 585–93. http://dx.doi.org/10.1016/0959-437x(93)90094-6.
Pełny tekst źródłaBorman, W. H., i D. E. Yorde. "Analysis of chick somite myogenesis by in situ confocal microscopy of desmin expression." Journal of Histochemistry & Cytochemistry 42, nr 2 (luty 1994): 265–72. http://dx.doi.org/10.1177/42.2.8288867.
Pełny tekst źródłaOsborne, H. B., C. Gautier-Courteille, A. Graindorge, C. Barreau, Y. Audic, R. Thuret, N. Pollet i L. Paillard. "Post-transcriptional regulation in Xenopus embryos: role and targets of EDEN-BP". Biochemical Society Transactions 33, nr 6 (26.10.2005): 1541–43. http://dx.doi.org/10.1042/bst0331541.
Pełny tekst źródłaSparrow, D. B., W. C. Jen, S. Kotecha, N. Towers, C. Kintner i T. J. Mohun. "Thylacine 1 is expressed segmentally within the paraxial mesoderm of the Xenopus embryo and interacts with the Notch pathway". Development 125, nr 11 (1.06.1998): 2041–51. http://dx.doi.org/10.1242/dev.125.11.2041.
Pełny tekst źródłaWeisblat, David A. "Segmentation and commitment in the leech embryo". Cell 42, nr 3 (październik 1985): 701–2. http://dx.doi.org/10.1016/0092-8674(85)90264-8.
Pełny tekst źródłaDavidson, Duncan. "Segmentation in frogs". Development 104, Supplement (1.10.1988): 221–29. http://dx.doi.org/10.1242/dev.104.supplement.221.
Pełny tekst źródłaZhang, Kun, Hongbin Zhang, Huiyu Zhou, Danny Crookes, Ling Li, Yeqin Shao i Dong Liu. "Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model". Computational Intelligence and Neuroscience 2019 (3.02.2019): 1–14. http://dx.doi.org/10.1155/2019/8214975.
Pełny tekst źródłaMaia-Fernandes, Ana Cristina, Ana Martins-Jesus, Nísia Borralho-Martins, Tomás Pais-de-Azevedo, Ramiro Magno, Isabel Duarte i Raquel P. Andrade. "Spatio-temporal dynamics of early somite segmentation in the chicken embryo". PLOS ONE 19, nr 4 (18.04.2024): e0297853. http://dx.doi.org/10.1371/journal.pone.0297853.
Pełny tekst źródłaResende, Tatiana P., Raquel P. Andrade i Isabel Palmeirim. "Timing Embryo Segmentation: Dynamics and Regulatory Mechanisms of the Vertebrate Segmentation Clock". BioMed Research International 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/718683.
Pełny tekst źródłaRozprawy doktorskie na temat "Embryo segmentation"
Jaques, Karen F. "Segmentation and axonal guidance in the vertebrate embryo". Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386159.
Pełny tekst źródłaLim, Tit Meng. "Segmentation in the nervous system of the chick embryo". Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329053.
Pełny tekst źródłaVermeren, Matthieu M. "Molecular basis of peripheral nerve segmentation in the chick embryo". Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621857.
Pełny tekst źródłaShah, Sheetal Mansukhlal. "Genetic and molecular studies of segmentation and axon guidance in Drosophila". Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312177.
Pełny tekst źródłaRajasekaran, Bhavna. "Analysis of Movement of Cellular Oscillators in the Pre-somitic Mesoderm of the Zebrafish Embryo". Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-110304.
Pełny tekst źródłaGenest, Diane. "Imaging of the fish embryo model and applications to toxicology". Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2008/document.
Pełny tekst źródłaNumerous chemicals are used as ingredients by the cosmetics industry and are included in cosmetics formula. Aside from the assessment of their efficacy, the cosmetics industry especially needs to assess the safety of their chemicals for human. Toxicological screening of chemicals is performed with the aim of revealing the potential toxic effect of the tested chemical. Among the potential effects we want to detect, the developmental toxicity of the chemical (teratogenicity), meaning its capability of provoking abnormalities during the embryonic development, is crucial. With respect to the international regulations that forbid the use of animal testing for the safety assessment of cosmetics, the toxicological assessment of chemicals must base on an ensemble of in silico assays, in vitro assays and alternative models based assays. For now, a few alternative methods have been validated in the field of developmental toxicology. The development of new alternative methods is thus required. In addition to the safety assessment, the environmental toxicity assessment is also required. The use of most of cosmetics and personal care products leads to their rejection in waterways after washing and rince. This results in the exposition of some aquatic environments (surface waters and coastal marine environments) to chemicals included in cosmetics and personal care products. Thus, the environmental assessment of cosmetics and of their ingredients requires the knowledge of their toxicity on organisms that are representative of aquatic food chains. In this context, the fish embryo model, which is ethically acceptable according to international regulations, presents a dual advantage for the cosmetics industry. Firstly, as a model representative of aquatic organisms, it is accurate for the environmental assessment of chemicals. Secondly, this model is promising for the assessment of the teratogenic effect of chemicals on human. For this reason, a teratogenicity assessment test is developed. This test is based on the analysis of medaka fish embryos (Oryzias Latipes) at 9 days post fertilization, after balneation in a predetermined concentration of the chemical under study. The analysis of functional and morphological parameters allows to calculate a teratogenicity index, that depends on both rates of dead and malformed embryos. This index allows to to draw a conclusion concerning the teratogenic effect of the chemical.The objective of this project is to automate the teratogenicity test, by automated image and video classification. A first method is developed that aims to automatically detect embryo heart beats from acquired video sequences. This method will allow to calculate the proportion of dead embryos. We then focus on the detection of two common malformations: axial malformations and absence of a swim bladder, based on a machine learning classification. This analysis must be completed by the detection of other malformations so that we can measure the rate of malformed embryos and thus, calculate the teratogenicity index of the tested chemical
Schaeffer, Julia. "The molecular regulation of spinal nerve outgrowth". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/271632.
Pełny tekst źródłaJanštová, Michaela. "Segmentace měkkých tkání v obličejové části myších embryí v mikrotomografických datech". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400988.
Pełny tekst źródłaKočendová, Kateřina. "Automatické vyhlazení 3D modelů kraniální embryonální myší chrupavky". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413111.
Pełny tekst źródłaNasser, Khalafallah Mahmoud Lamees. "A dictionary-based denoising method toward a robust segmentation of noisy and densely packed nuclei in 3D biological microscopy images". Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS283.pdf.
Pełny tekst źródłaCells are the basic building blocks of all living organisms. All living organisms share life processes such as growth and development, movement, nutrition, excretion, reproduction, respiration and response to the environment. In cell biology research, understanding cells structure and function is essential for developing and testing new drugs. In addition, cell biology research provides a powerful tool to study embryo development. Furthermore, it helps the scientific research community to understand the effects of mutations and various diseases. Time-Lapse Fluorescence Microscopy (TLFM) is one of the most appreciated imaging techniques which can be used in live-cell imaging experiments to quantify various characteristics of cellular processes, i.e., cell survival, proliferation, migration, and differentiation. In TLFM imaging, not only spatial information is acquired, but also temporal information obtained by repeating imaging of a labeled sample at specific time points, as well as spectral information, that produces up to five-dimensional (X, Y, Z + Time + Channel) images. Typically, the generated datasets consist of several (hundreds or thousands) images, each containing hundreds to thousands of objects to be analyzed. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel 3D denoising algorithm, based on unsupervised dictionary learning and sparse representation, that can both enhance very faint and noisy nuclei, in addition, it simultaneously detects nuclei position accurately. Furthermore, our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. The framework of the proposed method comprises image denoising, nuclei detection, and segmentation. In the denoising step, an initial dictionary is constructed by selecting random patches from the raw image then an iterative technique is implemented to update the dictionary and obtain the final one which is less noisy. Next, a detection map, based on the dictionary coefficients used to denoise the image, is used to detect marker points. Afterward, a thresholding-based approach is proposed to get the segmentation mask. Finally, a marker-controlled watershed approach is used to get the final nuclei segmentation result. We generate 3D synthetic images to study the effect of the few parameters of our method on cell nuclei detection and segmentation, and to understand the overall mechanism for selecting and tuning the significant parameters of the several datasets. These synthetic images have low contrast and low signal to noise ratio. Furthermore, they include touching spheres where these conditions simulate the same characteristics exist in the real datasets. The proposed framework shows that integrating our denoising method along with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, two datasets from the cell tracking challenge are extensively tested. Across all datasets, the proposed method achieved very promising results with 96.96% recall for the C.elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99.3%)
Książki na temat "Embryo segmentation"
Vaage, Sigmund. Segmentation of the Primitive Neural Tube in Chick Embryos: A Morphological, Histochemical and Autoradiographical Investigation. Springer London, Limited, 2013.
Znajdź pełny tekst źródłaCzęści książek na temat "Embryo segmentation"
Bagnall, Keith M. "Segmentation and Compartments in the Vertebrate Embryo". W Formation and Differentiation of Early Embryonic Mesoderm, 133–47. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3458-7_12.
Pełny tekst źródłaTrainor, Paul A., Miguel Manzanares i Robb Krumlauf. "Genetic Interactions During Hindbrain Segmentation in the Mouse Embryo". W Results and Problems in Cell Differentiation, 51–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-48002-0_3.
Pełny tekst źródłaBellairs, Ruth. "The Tail Bud and Cessation of Segmentation in the Chick Embryo". W Somites in Developing Embryos, 161–78. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-2013-3_13.
Pełny tekst źródłaKeynes, Roger J., i Claudio D. Stern. "Mesenchymal-Epithelial Interactions during Neural Segmentation in the Chick Embryo". W Mesenchymal-Epithelial Interactions in Neural Development, 309–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-71837-3_24.
Pełny tekst źródłaBastiaansen, Wietske A. P., Melek Rousian, Régine P. M. Steegers-Theunissen, Wiro J. Niessen, Anton Koning i Stefan Klein. "Atlas-Based Segmentation of the Human Embryo Using Deep Learning with Minimal Supervision". W Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, 211–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60334-2_21.
Pełny tekst źródłaOsmond, Mark. "The Effects of Retinoic Acid on Early Heart Formation and Segmentation in the Chick Embryo". W Formation and Differentiation of Early Embryonic Mesoderm, 275–300. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3458-7_23.
Pełny tekst źródłaMeinhardt, Hans. "Models of Segmentation". W Somites in Developing Embryos, 179–89. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-2013-3_14.
Pełny tekst źródłaKeynes, Roger, Geoffrey Cook, Jamie Davies, Paul Scotting, Wendie Norris, Claudio Stern i Andrew Lumsden. "Segmentation and Neuronal Development in Vertebrate Embryos". W Brain Repair, 213–24. London: Macmillan Education UK, 1990. http://dx.doi.org/10.1007/978-1-349-11358-3_17.
Pełny tekst źródłaKatona, Melinda, Tünde Tőkés, Emília Rita Szabó, Szilvia Brunner, Imre Zoltán Szabó, Róbert Polanek, Katalin Hideghéty i László G. Nyúl. "Automatic Segmentation and Quantitative Analysis of Irradiated Zebrafish Embryos". W Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, 95–107. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20805-9_9.
Pełny tekst źródłaChlyah, H., M. Hsaine, R. Karim i A. Chlyah. "Improvement of Somatic Embryogenesis in Wheat by Segmentation of Cultured Embryos". W Biotechnology in Agriculture and Forestry, 88–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-10933-5_5.
Pełny tekst źródłaStreszczenia konferencji na temat "Embryo segmentation"
Jamal, Ade, Aditya Pratama Dharmawan, Ali Akbar Septiandri, Pritta Amelia Iffanolida, Oki Riayati i Budi Wiweko. "Densely U-Net Models for Human Embryo Segmentation". W 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2023. http://dx.doi.org/10.1109/aidas60501.2023.10284599.
Pełny tekst źródłaKhan, Aisha, Stephen Gould i Mathieu Salzmann. "Segmentation of developing human embryo in time-lapse microscopy". W 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493417.
Pełny tekst źródłaZouagui, T., E. Chereul, C. Odet i M. Janier. "Mouse embryo’s heart segmentation on μMRI acquisitions". W 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555372.
Pełny tekst źródłaSaifullah, Shoffan, i Andiko P. Suryotomo. "Thresholding and Hybrid CLAHE-HE for Chicken Egg Embryo Segmentation". W 2021 International Conference on Communication & Information Technology (ICICT). IEEE, 2021. http://dx.doi.org/10.1109/icict52195.2021.9568444.
Pełny tekst źródłaSidhu, Simarjot S., i James K. Mills. "Automated Blastomere Segmentation for Early-Stage Embryo Using 3D Imaging Techniques". W 2019 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2019. http://dx.doi.org/10.1109/icma.2019.8816615.
Pełny tekst źródłaJen-Wei Kuo, Yao Wang, Orlando Aristizabal, Jeffrey A. Ketterling i Jonathan Mamou. "Automatic mouse embryo brain ventricle segmentation from 3D 40-MHz ultrasound data". W 2013 IEEE International Ultrasonics Symposium (IUS). IEEE, 2013. http://dx.doi.org/10.1109/ultsym.2013.0454.
Pełny tekst źródłaZanella, Cecilia, Barbara Rizzi, Camilo Melani, Matteo Campana, Paul Bourgine, Karol Mikula, Nadine Peyrieras i Alessandro Sarti. "Segmentation of Cells from 3-D Confocal Images of Live Zebrafish Embryo". W 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353722.
Pełny tekst źródłaZacharia, E., M. Bondesson, A. Riu, N. A. Ducharme, J. Gustafsson i I. A. Kakadiaris. "Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo". W 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091993.
Pełny tekst źródłaCorpuz, Shaun, i Aaron T. Ohta. "Deep Neural Network Segmentation of Embryo Inner Cell Mass and Trophectoderm Epithelium". W 2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED). IEEE, 2023. http://dx.doi.org/10.1109/nanomed59780.2023.10404589.
Pełny tekst źródłaKheradmand, S., A. Singh, P. Saeedi, J. Au i J. Havelock. "Inner cell mass segmentation in human HMC embryo images using fully convolutional network". W 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296582.
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