Automatic segmentation of lung findings in CT and application to Long COVID
This work addresses lung disease diagnosis, particularly for long COVID patients, but is incremental as it builds on a previous method.
The authors tackled automated segmentation of lung abnormalities in CT scans by proposing S-MEDSeg, an improved deep learning method that achieved significantly better segmentation performance compared to a baseline approach, and applied it to study lung findings in long COVID patients.
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.