Automatic segmentation of lung findings in CT and application to Long
  COVID
Diedre S. Carmo Rosarie A. Tudas Alejandro P. Comellas Leticia Rittner Roberto A. Lotufo Joseph M. Reinhardt Sarah E. Gerard
Automated segmentation of lung abnormalities in computed tomography is an
important step for diagnosing and characterizing lung disease. In this work, we
improve upon a previous method and propose S-MEDSeg, a deep learning based
approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg
combines a pre-trained EfficientNet backbone, bidirectional feature pyramid
network, and modern network advancements to achieve improved segmentation
performance. A comprehensive ablation study was performed to evaluate the
contribution of the proposed network modifications. The results demonstrate
modifications introduced in S-MEDSeg significantly improves segmentation
performance compared to the baseline approach. The proposed method is applied
to an independent dataset of long COVID inpatients to study the effect of
post-acute infection vaccination on extent of lung findings. Open-source code,
graphical user interface and pip package are available at
https://github.com/MICLab-Unicamp/medseg.