  Deep neural networks with multilevel connections process input data in
complex ways to learn the information.A networks learning efficiency depends
not only on the complex neural network architecture but also on the input
training images.Medical image segmentation with deep neural networks for skull
stripping or tumor segmentation from magnetic resonance images enables learning
both global and local features of the images.Though medical images are
collected in a controlled environment,there may be artifacts or equipment based
variance that cause inherent bias in the input set.In this study, we
investigated the correlation between the image quality metrics of MR images
with the neural network segmentation accuracy.For that we have used the 3D
DenseNet architecture and let the network trained on the same input but
applying different methodologies to select the training data set based on the
IQM values.The difference in the segmentation accuracy between models based on
the random training inputs with IQM based training inputs shed light on the
role of image quality metrics on segmentation accuracy.By running the image
quality metrics to choose the training inputs,further we may tune the learning
efficiency of the network and the segmentation accuracy.
