论文标题
使用各种数据推出从胸部X射线进行肺部分割
Lung Segmentation from Chest X-rays using Variational Data Imputation
论文作者
论文摘要
肺部不适化是许多呼吸疾病引起的肺部炎症，包括2019年新型电晕病毒疾病（Covid-19）。具有这种不透明的胸部X射线（CXR）使肺部的区域无法察觉，因此很难对其进行自动图像分析。在这项工作中，我们专注于从此类异常CXRS分割肺部，这是旨在从CXRS对Covid-19自动风险评分的管道的一部分。我们将高不透明度区域视为缺少数据，并提出了基于CNN的基于修改的图像分割网络，该网络利用深层生成模型进行数据插补。我们在正常的CXR上训练该模型，并具有广泛的数据增强，并证明了该模型扩展到极为异常的病例的有用性。
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.
