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In this work we propose to segment the prostate on a challenging dataset oftrans-rectal ultrasound (TRUS) images using convolutional neural networks(CNNs) and statistical shape models (SSMs). TRUS is commonly used for a numberof image-guided interventions on the prostate. Fast and accurate segmentationon the organ in these images is crucial to planning and fusion with othermodalities such as magnetic resonance images (MRIs) . However, TRUS has limitedsoft tissue contrast and signal to noise ratio which makes the task ofsegmenting the prostate challenging and subject to inter-observer andintra-observer variability. This is especially problematic at the base and apexwhere the gland boundary is hard to define. In this paper, we aim to tacklethis problem by taking advantage of shape priors learnt on an MR dataset whichhas higher soft tissue contrast allowing the prostate to be contoured moreaccurately. We use this shape prior in combination with a prostate tissueprobability map computed by a CNN for segmentation.

Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks