Journal
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
Volume 1, Issue 4, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2632-2153/aba8e8
Keywords
deep neural networks; microscopy; segmentation
Categories
Funding
- National Science Foundation [EB00303, CBET1066661]
- Erling-Persson Family Foundation
- Swedish Research Council [8651]
- Stockholm City Council [20170133, Alf 20150423]
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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.
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