BREAK：通过gEodesic变换和sKeleton嵌入重建支气管
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
摘要：
气道分割对于虚拟支气管镜检查和计算机辅助肺部疾病分析至关重要。近年来，卷积神经网络（CNN）已被广泛用于描绘支气管树。然而，基于CNN的方法的分割结果通常包括许多不连续的分支，这在临床使用中需要手动修复。断裂的一个主要原因是气道壁的外观可能受到肺部疾病以及血管邻近的影响，而网络往往过拟合训练集中的这些特殊模式。为了学习这些区域的鲁棒特征，我们设计了一个多分支框架，该框架采用测地线距离变换来捕获气道腔和壁之间的强度变化。另一个原因是班级内部的不平衡。由于外周支气管的体积可能比输入片中的大分支小得多，因此常见的分割损失对远端分支之间的断裂不敏感。因此，本文设计了一个破损敏感的正则化项，可以很容易地与其他损失函数相结合。在公开的数据集上进行了大量的实验。与最先进的方法相比，我们的框架可以检测到更多的分支，同时保持有竞争力的分割性能。
英文摘要：
Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. However, the segmentation results of the CNN-based methods usually include many discontinuous branches, which need manual repair in clinical use. A major reason for the breakages is that the appearance of the airway wall can be affected by the lung disease as well as the adjacency of the vessels, while the network tends to overfit to these special patterns in the training set. To learn robust features for these areas, we design a multi-branch framework that adopts the geodesic distance transform to capture the intensity changes between airway lumen and wall. Another reason for the breakages is the intra-class imbalance. Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches. Therefore, in this paper, a breakage-sensitive regularization term is designed and can be easily combined with other loss functions. Extensive experiments are conducted on publicly available datasets. Compared with state-of-the-art methods, our framework can detect more branches while maintaining competitive segmentation performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
