  The COVID-19 pandemic response highlighted the potential of deep learning
methods in facilitating the diagnosis, prognosis and understanding of lung
diseases through automated segmentation of pulmonary structures and lesions in
chest computed tomography (CT). Automated separation of lung lesion into
ground-glass opacity (GGO) and consolidation is hindered due to the
labor-intensive and subjective nature of this task, resulting in scarce
availability of ground truth for supervised learning. To tackle this problem,
we propose MEDPSeg. MEDPSeg learns from heterogeneous chest CT targets through
hierarchical polymorphic multitask learning (HPML). HPML explores the
hierarchical nature of GGO and consolidation, lung lesions, and the lungs, with
further benefits achieved through multitasking airway and pulmonary artery
segmentation. Over 6000 volumetric CT scans from different partially labeled
sources were used for training and testing. Experiments show PML enabling new
state-of-the-art performance for GGO and consolidation segmentation tasks. In
addition, MEDPSeg simultaneously performs segmentation of the lung parenchyma,
airways, pulmonary artery, and lung lesions, all in a single forward
prediction, with performance comparable to state-of-the-art methods specialized
in each of those targets. Finally, we provide an open-source implementation
with a graphical user interface at https://github.com/MICLab-Unicamp/medpseg.
