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.
翻译:在许多医学图像分析应用中,自动化地表分解是重要和具有挑战性的。最近为各种物体分解任务开发了基于深层学习的方法,其中多数是基于分类的方法,例如U-net,它预测每个氧化物成为目标对象或背景的概率。这些方法中有一个问题缺乏对分解物体的地形保障,通常需要事后处理才能推断物体的边界表面。本文提议以进化神经网络为基础的新模型,然后是可学习的表面平滑块,以便通过端到端培训解决表面分解问题。根据我们的知识,这是第一次与CNN在端到端之前学习平滑性的研究,以便直接进行地面分解,实现全球最佳性。在光谱多光谱光学一致性成声学实验(SD-OCT)的视波层分解和穿心超声波(IVUS)船只壁分割实验显示了非常有希望的结果。