  In this work we propose a novel approach to perform segmentation by
leveraging the abstraction capabilities of convolutional neural networks
(CNNs). Our method is based on Hough voting, a strategy that allows for fully
automatic localisation and segmentation of the anatomies of interest. This
approach does not only use the CNN classification outcomes, but it also
implements voting by exploiting the features produced by the deepest portion of
the network. We show that this learning-based segmentation method is robust,
multi-region, flexible and can be easily adapted to different modalities. In
the attempt to show the capabilities and the behaviour of CNNs when they are
applied to medical image analysis, we perform a systematic study of the
performances of six different network architectures, conceived according to
state-of-the-art criteria, in various situations. We evaluate the impact of
both different amount of training data and different data dimensionality (2D,
2.5D and 3D) on the final results. We show results on both MRI and transcranial
US volumes depicting respectively 26 regions of the basal ganglia and the
midbrain.
