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The COVID-19 pandemic response highlighted the potential of deep learningmethods in facilitating the diagnosis, prognosis and understanding of lungdiseases through automated segmentation of pulmonary structures and lesions inchest computed tomography (CT). Automated separation of lung lesion intoground-glass opacity (GGO) and consolidation is hindered due to thelabor-intensive and subjective nature of this task, resulting in scarceavailability of ground truth for supervised learning. To tackle this problem,we propose MEDPSeg. MEDPSeg learns from heterogeneous chest CT targets throughhierarchical polymorphic multitask learning (HPML). HPML explores thehierarchical nature of GGO and consolidation, lung lesions, and the lungs, withfurther benefits achieved through multitasking airway and pulmonary arterysegmentation. Over 6000 volumetric CT scans from different partially labeledsources were used for training and testing. Experiments show PML enabling newstate-of-the-art performance for GGO and consolidation segmentation tasks. Inaddition, MEDPSeg simultaneously performs segmentation of the lung parenchyma,airways, pulmonary artery, and lung lesions, all in a single forwardprediction, with performance comparable to state-of-the-art methods specializedin each of those targets. Finally, we provide an open-source implementationwith a graphical user interface at https://github.com/MICLab-Unicamp/medpseg.

MEDPSeg: Hierarchical polymorphic multitask learning for the  segmentation of ground-glass opacities, consolidation, and pulmonary  structures on computed tomography