  We propose a novel automatic method for accurate segmentation of the prostate
in T2-weighted magnetic resonance imaging (MRI). Our method is based on
convolutional neural networks (CNNs). Because of the large variability in the
shape, size, and appearance of the prostate and the scarcity of annotated
training data, we suggest training two separate CNNs. A global CNN will
determine a prostate bounding box, which is then resampled and sent to a local
CNN for accurate delineation of the prostate boundary. This way, the local CNN
can effectively learn to segment the fine details that distinguish the prostate
from the surrounding tissue using the small amount of available training data.
To fully exploit the training data, we synthesize additional data by deforming
the training images and segmentations using a learned shape model. We apply the
proposed method on the PROMISE12 challenge dataset and achieve state of the art
results. Our proposed method generates accurate, smooth, and artifact-free
segmentations. On the test images, we achieve an average Dice score of 90.6
with a small standard deviation of 2.2, which is superior to all previous
methods. Our two-step segmentation approach and data augmentation strategy may
be highly effective in segmentation of other organs from small amounts of
annotated medical images.
