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