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. * Corresponding author URL: fausto.milletari@tum.de (Fausto Milletari) 1 Fausto Milletari and Seyed-Ahmad Ahmadi contributed equally to this work.

The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

Abstract. 3D ultrasound segmentation is a challenging task due to image artefacts, low signal-to-noise ratio and lack of contrast at anatomical boundaries. Current solutions usually rely on complex, anatomy-specific regularization methods to improve segmentation accuracy. In this work, we propose a highly adaptive learning-based method for fully automatic segmentation of ultrasound volumes. During training, anatomy-specific features are obtained through a sparse auto-encoder. The extracted features are employed in a Hough Forest based framework to retrieve the position of the target anatomy and its segmentation contour. The resulting method is fully automatic, i.e. it does not require any human interaction, and can robustly and automatically adapt to different anatomies yet enforcing appearance and shape constraints. We demonstrate the performance of the method for three different applications: segmentation of midbrain, left ventricle of the heart and prostate.

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