  Automated surface segmentation is important and challenging in many medical
image analysis applications. Recent deep learning based methods have been
developed for various object segmentation tasks. Most of them are a
classification based approach (e.g., U-net), which predicts the probability of
being target object or background for each voxel. One problem of those methods
is lacking of topology guarantee for segmented objects, and usually post
processing is needed to infer the boundary surface of the object. In this
paper, a novel model based on 3-D convolutional neural networks (CNNs) and
Conditional Random Fields (CRFs) is proposed to tackle the surface segmentation
problem with end-to-end training. To the best of our knowledge, this is the
first study to apply a 3-D neural network with a CRFs model for direct surface
segmentation. Experiments carried out on NCI-ISBI 2013 MR prostate dataset and
Medical Segmentation Decathlon Spleen dataset demonstrated promising
segmentation results.
