Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
Year: 2020
Type: article
Abstract: Deep neural networks (DNN) have been widely used to carry out segmentation tasks in both electron microscopy (EM) and light/fluorescence microscopy (LM/FM). Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show how Res-CR-Net, a new type of fully convolutional neural network that does not adopt a U-Net architecture, excels at segmentation tasks traditionally considered very hard, like recognizing the contours of nuclei, cytoplasm and mitochondria in densely packed cells in either EM or LM/FM images.
Authors: Hassan Abdallah, Brent Formosa, Asiri Liyanaarachchi, Maranda Saigh, Samantha Silvers, Suzan Arslanturk, Douglas J Taatjes, Lars Larsson, Bhanu P Jena, Domenico L Gatti
Institution: Wayne State University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W3017098301