Figures
Application of convolutional neural networks for image segmentation of biological datasets
The image demonstrates application of DeepMIB software for segmentation of 2D and 3D light and electron microscopy datasets. DeepMIB can train convolutional neural networks and apply them to segment multidimensional biological datasets. DeepMIB comes in a single, easy to install package bundled with Microscopy Image Browser. Its utilization does not require programming skills nor large knowledge about deep learning and, thus, is suitable for everyone. Belevich and Jokitalo (2021)
Image Credit: Ilya Belevich
Citation: (2021) PLoS Computational Biology Issue Image | Vol. 17(3) March 2021. PLoS Comput Biol 17(3): ev17.i03. https://doi.org/10.1371/image.pcbi.v17.i03
Published: March 31, 2021
Copyright: © 2021 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The image demonstrates application of DeepMIB software for segmentation of 2D and 3D light and electron microscopy datasets. DeepMIB can train convolutional neural networks and apply them to segment multidimensional biological datasets. DeepMIB comes in a single, easy to install package bundled with Microscopy Image Browser. Its utilization does not require programming skills nor large knowledge about deep learning and, thus, is suitable for everyone. Belevich and Jokitalo (2021)
Image Credit: Ilya Belevich