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Automated segmentation of lung abnormalities in computed tomography is animportant step for diagnosing and characterizing lung disease. In this work, weimprove upon a previous method and propose S-MEDSeg, a deep learning basedapproach for accurate segmentation of lung lesions in chest CT images. S-MEDSegcombines a pre-trained EfficientNet backbone, bidirectional feature pyramidnetwork, and modern network advancements to achieve improved segmentationperformance. A comprehensive ablation study was performed to evaluate thecontribution of the proposed network modifications. The results demonstratemodifications introduced in S-MEDSeg significantly improves segmentationperformance compared to the baseline approach. The proposed method is appliedto an independent dataset of long COVID inpatients to study the effect ofpost-acute infection vaccination on extent of lung findings. Open-source code,graphical user interface and pip package are available athttps://github.com/MICLab-Unicamp/medseg.

Automatic segmentation of lung findings in CT and application to Long COVID