Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.

中文翻译：

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来自合成误差增强的标签细化网络，用于医学图像分割


用于图像分割的深度卷积神经网络不会显式学习标签结构，并且可能会产生结构不正确的分割，例如，在气道或血管等树状结构的分割中使用断开连接的圆柱形结构。在本文中，我们提出了一种新的标签细化方法，从初始分割中纠正此类错误，隐式地结合了有关标签结构的信息。该方法具有两个新颖的部分：（1） 生成合成结构错误的模型，以及 （2） 标签外观仿真网络，该网络生成具有外观与实际初始分割相似的合成错误的分割。使用这些带有合成错误和原始图像的分割，对标签细化网络进行训练以纠正错误并改进初始分割。所提出的方法在两项分割任务上得到了验证：胸部计算机断层扫描 （CT） 扫描的气道分割和大脑 3D CT 血管造影 （CTA） 图像的脑血管分割。在这两种应用中，我们的方法都明显优于标准的 3D U-Net、之前的四种标签细化方法以及使用为管状结构量身定制的损失训练的 U-Net。当使用额外的未标记数据进行模型训练时，改进甚至更大。在消融研究中，我们证明了所提出的方法的不同组成部分的价值。