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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Praeger, Matthew
University of Southampton
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2021Laser Induced Backwards Transfer (LIBT) of graphene onto glass
- 2020Microscale deposition of 2D materials via laser induced backwards transfer
- 2020Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learningcitations
- 2019Automated 3D labelling of fibroblasts in SEM-imaged placenta using deep learning
- 2017The effects of water on the dielectric properties of aluminum based nanocompositescitations
- 2017On the effect of functionalizer chain length and water content in polyethylene/silica nanocomposites: Part II – Charge Transportcitations
- 2017On the effect of functionalizer chain length and water content in polyethylene/silica nanocompositescitations
- 2017The effects of water on the dielectric properties of silicon based nanocompositescitations
- 2016Supporting data for "The effects of water on the dielectric properties of silicon based nanocomposites"
- 2015The effects of surface hydroxyl groups in polyethylene-silica nanocomposites
- 2014Dielectric studies of polystyrene-based, high-permittivity composite systemscitations
- 2014Effect of water absorption on dielectric properties of nano-silica/polyethylene compositescitations
- 2014A simple theoretical model for the bulk properties of nanocomposite materialscitations
- 2014Barium titanate and the dielectric response of polystyrene-based composites
- 2013A dielectric spectroscopy study of the polystyrene/nanosilica model system
- 2013Nano-Silica Filled Polystyrene: Correlating DC Breakdown Strength and Particle Agglomeration.
- 2013The breakdown strength and localised structure of polystyrene as a function of nanosilica fill-fraction
- 2012Fabrication of nanoscale glass fibers by electrospinningcitations
Places of action
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document
Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning
Abstract
Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.