Gomez peer linux
![gomez peer linux gomez peer linux](http://www.kaosklub.com/wp-content/uploads/2012/06/estrategia-blog-censured1-1024x299.jpg)
All the authors contributed to the conception of the study, the design of the experimental framework and took part in the literature review. wrote the manuscript with help from E.L., M.U., A.M.-B. worked on the synchronization with the BioImage Model Zoo.
#Gomez peer linux code#
wrote the code lines of the supplementary Python notebooks, Python library and ImageJ macros. built the connection between the toolbox and ImJoy. developed and implemented the toolbox and worked on the supporting documentation with input from the rest of the authors. contributed to the design of the experimental framework, and reviewed, trained and exported existing image processing methods. We also want to acknowledge the support of NVIDIA Corporation with the donation of the Titan X (Pascal) GPU card used for this research (A.M.B.).Į.G.-M. We thank the program ‘Short Term Scientific Missions’ of NEUBIAS (network of European bioimage analysts) (E.G.M., C.G.L.H.).
![gomez peer linux gomez peer linux](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10207-018-0404-6/MediaObjects/10207_2018_404_Fig13_HTML.gif)
We would like to thank the Science for Life Laboratory, Erling-Persson Foundation and Knut and Alice Wallenberg foundation (grant no. This work was also supported by the EPFL Center for Imaging (C.G.L.H., L.D., D.S., M.U.). We acknowledge the support of Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant nos TEC2016-78052-R and PID2019-109820RB-I00, MINECO/FEDER, UE, co-financed by European Regional Development Fund (ERDF), ‘A way of making Europe’ (E.G.M., C.G.L.H., A.M.B.), and a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (A.M.B.). We would like specially to mention all the contributors and community partners at the BioImage Model Zoo for the time they have spent to get a cross-compatible model format. Bollmann for including deepImageJ in their tutorials. We would also like to thank NEUBIAS for supporting the project, the NEUBIAS symposium and NEUBIAS and P. Möhl (YAPIC) for the fruitful discussions and enriching feedback about the deepImageJ project. González-Obando (tested the beta-versions), C. Arganda-Carreras (tested the beta-versions), D. ImageJ2: ImageJ for the next generation of scientific image data.
![gomez peer linux gomez peer linux](https://www.oregonlive.com/resizer/jbmL69LNHOJ5atvXATI50o6FcCg=/1280x0/smart/cloudfront-us-east-1.images.arcpublishing.com/advancelocal/OJ6D6NBTJ5GYHKKCJKLU72C2YU.png)
Fiji: an open-source platform for biological-image analysis. in Bioimage Analysis Components and Workflows (eds Sladoje, N. Gómez-de-Mariscal, E., Franco, D., Muñoz-Barrutia, A. Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning. Deep-learning-based segmentation of small extracellular vesicles in transmission electron microscopy images. Nucleus segmentation across imaging experiments: the 2018 data science bowl. Deep-STORM: super-resolution single-molecule microscopy by deep learning. U-Net: deep learning for cell counting, detection, and morphometry. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 – 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part II 265–273 (Springer, 2018). Cell detection with star-convex polygons. NIH Image to ImageJ: 25 years of image analysis. Quantitative digital microscopy with deep learning. Cryo-electron tomography workflows for quantitative analysis of actin networks involved in cell migration. Democratising deep learning for microscopy with ZeroCostDL4Mic. ImJoy: an open-source computational platform for the deep learning era. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. Ilastik: interactive machine learning for (bio)image analysis. DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification. Inés, A., Domínguez, C., Heras, J., Mata, E. Content-aware image restoration: pushing the limits of fluorescence microscopy.
#Gomez peer linux software#
Open-source deep-learning software for bioimage segmentation. The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Deep learning for cellular image analysis. A bird’s-eye view of deep learning in bioimage analysis.