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Automated Training of Deep Convolutional Neural Networks for Cell Segmentation.

ABSTRACT: 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.

SUBMITTER: Sadanandan SK 

PROVIDER: S-EPMC5552800 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Automated Training of Deep Convolutional Neural Networks for Cell Segmentation.

Sadanandan Sajith Kecheril SK   Ranefall Petter P   Le Guyader Sylvie S   Wählby Carolina C  

Scientific reports 20170810 1

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. ...[more]