  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 convolutional neural network (CNN) followed by a
learnable surface smoothing block 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 learn smoothness priors end-to-end with CNN for
direct surface segmentation with global optimality. Experiments carried out on
Spectral Domain Optical Coherence Tomography (SD-OCT) retinal layer
segmentation and Intravascular Ultrasound (IVUS) vessel wall segmentation
demonstrated very promising results.
