Automatic segmentation of lung findings in CT and application to Long COVID
arXiv:2310.09446v1 [eess.IV] 13 Oct 2023
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.
Full paper here: https://doi.org/10.48550/arXiv.2310.09446
- Authors: Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas, Leticia Rittner, Roberto A. Lotufo, Joseph M. Reinhardt, Sarah E. Gerard
- How to cite: CARMO, Diedre S. et al. Automatic segmentation of lung findings in CT and application to Long COVID. arXiv preprint arXiv:2310.09446, 2023.
- Published: 13 October 2023