  The most recent fast and accurate image segmentation methods are built upon
fully convolutional deep neural networks. In this paper, we propose new deep
learning strategies for DenseNets to improve segmenting images with subtle
differences in intensity values and features. We aim to segment brain tissue on
infant brain MRI at about 6 months of age where white matter and gray matter of
the developing brain show similar T1 and T2 relaxation times, thus appear to
have similar intensity values on both T1- and T2-weighted MRI scans. Brain
tissue segmentation at this age is, therefore, very challenging. To this end,
we propose an exclusive multi-label training strategy to segment the mutually
exclusive brain tissues with similarity loss functions that automatically
balance the training based on class prevalence. Using our proposed training
strategy based on similarity loss functions and patch prediction fusion we
decrease the number of parameters in the network, reduce the complexity of the
training process focusing the attention on less number of tasks, while
mitigating the effects of data imbalance between labels and inaccuracies near
patch borders. By taking advantage of these strategies we were able to perform
fast image segmentation (90 seconds per 3D volume), using a network with less
parameters than many state-of-the-art networks, overcoming issues such as
3Dvs2D training and large vs small patch size selection, while achieving the
top performance in segmenting brain tissue among all methods tested in first
and second round submissions of the isointense infant brain MRI segmentation
(iSeg) challenge according to the official challenge test results. Our proposed
strategy improves the training process through balanced training and by
reducing its complexity while providing a trained model that works for any size
input image and is fast and more accurate than many state-of-the-art methods.
