Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automati...

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Bibliographic Details
Published in:Scientific reports Vol. 7; no. 1; pp. 7860 - 7
Main Authors: Sadanandan, Sajith Kecheril, Ranefall, Petter, Le Guyader, Sylvie, Wählby, Carolina
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 10.08.2017
Nature Publishing Group
Nature Portfolio
Subjects:
14
14/63
631/114/1564
631/80/2373
Computerized Image Processing
Datoriserad bildbehandling
Humanities and Social Sciences
Image processing
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Segmentation
Training
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-07599-6