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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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Mills, Benjamin
University of Southampton
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 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
- 2019Image-based monitoring of high-precision laser machining via a convolutional neural network
- 2017Time-resolved imaging of flyer dynamics for femtosecond laser-induced backward transfer of solid polymer thin filmscitations
- 2017Laser fabricated nanofoam from polymeric substrates
- 2015Dynamic spatial pulse shaping via a digital micromirror device for patterned laser-induced forward transfer of solid polymer filmscitations
- 2014Femtosecond multi-level phase switching in chalcogenide thin films for all-optical data and image processing
- 2013Printing of continuous copper lines using LIFT with donor replenishment
- 2013Chalcogenide-based phase-change metamaterials for all-optical, high-contrast switching in a fraction of a wavelength
- 2009Nanomaterial structure determination using XUV diffraction
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.