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Automated surface segmentation is important and challenging in many medicalimage analysis applications. Recent deep learning based methods have beendeveloped for various object segmentation tasks. Most of them are aclassification based approach, e.g. U-net, which predicts the probability ofbeing target object or background for each voxel. One problem of those methodsis lacking of topology guarantee for segmented objects, and usually postprocessing is needed to infer the boundary surface of the object. In thispaper, a novel model based on convolutional neural network (CNN) followed by alearnable surface smoothing block is proposed to tackle the surfacesegmentation 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 fordirect surface segmentation with global optimality. Experiments carried out onSpectral Domain Optical Coherence Tomography (SD-OCT) retinal layersegmentation and Intravascular Ultrasound (IVUS) vessel wall segmentationdemonstrated very promising results.

Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors