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In this work we propose a novel approach to perform segmentation byleveraging the abstraction capabilities of convolutional neural networks(CNNs). Our method is based on Hough voting, a strategy that allows for fullyautomatic localisation and segmentation of the anatomies of interest. Thisapproach does not only use the CNN classification outcomes, but it alsoimplements voting by exploiting the features produced by the deepest portion ofthe network. We show that this learning-based segmentation method is robust,multi-region, flexible and can be easily adapted to different modalities. Inthe attempt to show the capabilities and the behaviour of CNNs when they areapplied to medical image analysis, we perform a systematic study of theperformances of six different network architectures, conceived according tostate-of-the-art criteria, in various situations. We evaluate the impact ofboth 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 transcranialUS volumes depicting respectively 26 regions of the basal ganglia and themidbrain.

Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound