  The anatomical location of imaging features is of crucial importance for
accurate diagnosis in many medical tasks. Convolutional neural networks (CNN)
have had huge successes in computer vision, but they lack the natural ability
to incorporate the anatomical location in their decision making process,
hindering success in some medical image analysis tasks.
  In this paper, to integrate the anatomical location information into the
network, we propose several deep CNN architectures that consider multi-scale
patches or take explicit location features while training. We apply and compare
the proposed architectures for segmentation of white matter hyperintensities in
brain MR images on a large dataset. As a result, we observe that the CNNs that
incorporate location information substantially outperform a conventional
segmentation method with hand-crafted features as well as CNNs that do not
integrate location information. On a test set of 46 scans, the best
configuration of our networks obtained a Dice score of 0.791, compared to 0.797
for an independent human observer. Performance levels of the machine and the
independent human observer were not statistically significantly different
(p-value=0.17).
