  Deep neural networks have shown great achievements in solving complex
problems. However, there are fundamental problems that limit their real world
applications. Lack of measurable criteria for estimating uncertainty in the
network outputs is one of these problems. In this paper, we address this
limitation by introducing deformation to the network input and measuring the
level of stability in the network's output. We calculate simple random
transformations to estimate the prediction uncertainty of deep convolutional
neural networks. For a real use-case, we apply this method to left ventricle
segmentation in MRI cardiac images. We also propose an adaptive thresholding
method to consider the deep neural network uncertainty. Experimental results
demonstrate state-of-the-art performance and highlight the capabilities of
simple methods in conjunction with deep neural networks.
