Sadanandan SK, Ranefall P, Le Guyader S, Wählby C
Sci Rep 7 (1) 7860 [2017-08-10; online 2017-08-10]
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.