  In this paper, we present a generic deep convolutional neural network (DCNN)
for multi-class image segmentation. It is based on a well-established
supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by
adding widely used batch normalization and residual block (named as BRU-net) to
improve the efficiency of model training. Based on BRU-net, we further
introduce a dynamically weighted cross-entropy loss function. The weighting
scheme is calculated based on the pixel-wise prediction accuracy during the
training process. Assigning higher weights to pixels with lower segmentation
accuracies enables the network to learn more from poorly predicted image
regions. Our method is named as feedback weighted U-net (FU-net). We have
evaluated our method based on T1- weighted brain MRI for the segmentation of
midbrain and substantia nigra, where the number of pixels in each class is
extremely unbalanced to each other. Based on the dice coefficient measurement,
our proposed FU-net has outperformed BRU-net and U-net with statistical
significance, especially when only a small number of training examples are
available. The code is publicly available in GitHub (GitHub link:
https://github.com/MinaJf/FU-net).
