  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.
