The COVID-19 pandemic response highlighted the potential of deep learning methods in facilitating the diagnosis and prognosis of lung diseases through automated segmentation of normal and abnormal tissue in computed tomography (CT). Such methods not only have the potential to aid in clinical decision-making but also contribute to the comprehension of novel diseases. In light of the labor-intensive nature of manual segmentation for large chest CT cohorts, there is a pressing need for reliable automated approaches that enable efficient analysis of chest CT anatomy in vast research databases, especially in more scarcely annotated targets such as pneumonia consolidations. A limiting factor for the development of such methods is that most current models optimize a fixed annotation format per network output. To tackle this problem, polymorphic training is used to optimize a network with a fixed number of output channels to represent multiple hierarchical anatomic structures, indirectly optimizing more complex labels with simpler annotations. We combined over 6000 volumetric CT scans containing varying formats of manual and automated labels from different sources, and used polymorphic training along with multitask learning to develop MEDPSeg, an end-to-end method for the segmentation of lungs, airways, pulmonary artery, and lung lesions with separation of ground glass opacities, and parenchymal consolidations, all in a single forward prediction. We achieve state-of-the-art performance in multiple targets, particularly in the segmentation of ground glass opacities and consolidations, a challenging problem with limited manual annotation availability. In addition, we provide an open-source implementation with a graphical user interface at https://github.com/MICLab-Unicamp/medpseg.

COVID-19大流行病反应凸显了深度学习方法在通过计算机断层扫描(CT)对肺部疾病的正常和异常组织进行自动分割方面的潜力，这些方法不仅有助于临床决策，还有助于理解新型疾病。为了解决手动分割大型CT数据库的繁重性质，我们使用多态训练优化了一个网络，以表示多个分层解剖结构，间接优化了更复杂的标签。我们结合来自不同来源的6000多个不同格式的手动和自动标签的CT扫描，使用多任务学习和多态训练开发了MEDPSeg，一种用于单向预测肺部、气道、肺动脉和肺病变分割的端到端方法。我们在多个目标中取得了最先进的性能，特别是在地面玻璃浑浊度和病变分割方面，这是一个具有有限手动注释可用性的具有挑战性的问题。此外，我们提供了一个开源实现，具有图形用户界面的链接：https://此处为URL。

MEDPSeg：基于计算机断层扫描的肺部结构和病变的端到端分割