  Deep learning has successfully been leveraged for medical image segmentation.
It employs convolutional neural networks (CNN) to learn distinctive image
features from a defined pixel-wise objective function. However, this approach
can lead to less output pixel interdependence producing incomplete and
unrealistic segmentation results. In this paper, we present a fully automatic
deep learning method for robust medical image segmentation by formulating the
segmentation problem as a recurrent framework using two systems. The first one
is a forward system of an encoder-decoder CNN that predicts the segmentation
result from the input image. The predicted probabilistic output of the forward
system is then encoded by a fully convolutional network (FCN)-based context
feedback system. The encoded feature space of the FCN is then integrated back
into the forward system's feed-forward learning process. Using the FCN-based
context feedback loop allows the forward system to learn and extract more
high-level image features and fix previous mistakes, thereby improving
prediction accuracy over time. Experimental results, performed on four
different clinical datasets, demonstrate our method's potential application for
single and multi-structure medical image segmentation by outperforming the
state of the art methods. With the feedback loop, deep learning methods can now
produce results that are both anatomically plausible and robust to low contrast
images. Therefore, formulating image segmentation as a recurrent framework of
two interconnected networks via context feedback loop can be a potential method
for robust and efficient medical image analysis.
