论文标题
胸部X射线上的气胸和胸管分类，可检测丢失的气胸
Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax
论文作者
论文摘要
胸部X射线成像被广泛用于诊断气胸的诊断，并且对开发自动化方法有浓厚的兴趣来协助图像解释。我们提出了一个图像分类管道，该管道检测气胸以及通常用于治疗气胸的各种类型的胸管。我们的多阶段算法基于肺部分割，然后进行气胸分类，包括最有可能含有气胸的斑块的分类。该算法在开源基准数据集上实现了用于气动性分类的最先进的性能。与以前的工作不同，该算法在带有和没有胸管的数据上显示出可比的性能，因此具有改善的临床实用性。为了在现实的临床情况下评估这些算法，我们证明了在大型胸部X射线研究数据集中识别遗失气胸的真实病例的能力。
Chest x-ray imaging is widely used for the diagnosis of pneumothorax and there has been significant interest in developing automated methods to assist in image interpretation. We present an image classification pipeline which detects pneumothorax as well as the various types of chest tubes that are commonly used to treat pneumothorax. Our multi-stage algorithm is based on lung segmentation followed by pneumothorax classification, including classification of patches that are most likely to contain pneumothorax. This algorithm achieves state of the art performance for pneumothorax classification on an open-source benchmark dataset. Unlike previous work, this algorithm shows comparable performance on data with and without chest tubes and thus has an improved clinical utility. To evaluate these algorithms in a realistic clinical scenario, we demonstrate the ability to identify real cases of missed pneumothorax in a large dataset of chest x-ray studies.
