MEDPSeg: End-to-end segmentation of pulmonary structures and lesions in computed tomography

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.