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

• URL: http://arxiv.org/abs/2310.09446v1
• Date: Fri, 13 Oct 2023 23:42:43 GMT
• Title: Automatic segmentation of lung findings in CT and application to Long COVID
• Authors: Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas, Leticia Rittner, Roberto A. Lotufo, Joseph M. Reinhardt, Sarah E. Gerard
• Abstract summary: S-MEDSeg is 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.
• Score: 38.69538648742266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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