论文标题
全球最佳的表面细分，使用深度学习和可学习的平稳性先验
Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
论文作者
论文摘要
在许多医学图像分析应用中，自动表面细分很重要，而且具有挑战性。为各种对象细分任务开发了最近的基于深度学习的方法。其中大多数是基于分类的方法，例如U-NET，可以预测每个体素的目标对象或背景的概率。这些方法的一个问题是缺乏分段对象的拓扑保证，通常需要后处理来推断对象的边界表面。在本文中，提出了一个基于卷积神经网络（CNN）的新型模型，然后提出了可学习的表面平滑块，以通过端到端训练解决表面细分问题。据我们所知，这是第一项研究使用CNN端到端学习平滑度的研究，以全球最优性直接进行表面分割。在光谱结构域光学相干断层扫描（SD-OCT）视网膜层分割和血管内超声（IVUS）血管壁分割上进行的实验表现出非常有希望的结果。
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.
