Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
This work addresses the challenge of ensuring topology guarantees in medical image segmentation, which is important for applications like retinal layer and vessel wall analysis, though it appears incremental by building on existing deep learning methods.
The paper tackled the problem of automated surface segmentation in medical images by proposing a novel model combining a convolutional neural network with a learnable surface smoothing block for end-to-end training, achieving promising results on SD-OCT retinal layer and IVUS vessel wall segmentation tasks.
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.