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.
@article{arxiv.2310.09446,
title = {Automatic segmentation of lung findings in CT and application to Long COVID},
author = {Diedre S. Carmo and Rosarie A. Tudas and Alejandro P. Comellas and Leticia Rittner and Roberto A. Lotufo and Joseph M. Reinhardt and Sarah E. Gerard},
journal= {arXiv preprint arXiv:2310.09446},
year = {2023}
}
Comments
Versao em portugu\^es brasileiro submetida para o XV EADCA 2023. Brazilian portuguese verson submitted to XV EADCA 2023