Segmentation and reconstruction of the lung in CT affected by COVID-19 using deep learning and adaptive convex hull

Luan S. Silva; João O. B. Diniz; Giovanni L. F. da Silva; Aristófanes Corrêa Silva; Anselmo C. Paiva

doi:10.1117/12.2606289

10 December 2021 Segmentation and reconstruction of the lung in CT affected by COVID-19 using deep learning and adaptive convex hull

Luan S. Silva, João O. B. Diniz, Giovanni L. F. da Silva, Aristófanes Corrêa Silva, Anselmo C. Paiva

Author Affiliations +

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208808 (2021) https://doi.org/10.1117/12.2606289
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

In 2020, COVID-19 emerged, and several health units faced problems with the demand for chest Computed Tomography (CT), one of the ways of detecting lung lesions. One of the initial processes for treating CT is lung segmentation. Several computational methods have been proposed since they would accelerate the patient’s diagnosis process. This paper proposes a method for lung segmentation on CT in patients affected by COVID-19 using deep learning and adaptive convex hull. In this work, two databases were used: one with 38 and the other with 72 patients, without and with COVID-19, respectively. The promising results may assist in future work to detect pneumonia.

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Luan S. Silva, João O. B. Diniz, Giovanni L. F. da Silva, Aristófanes Corrêa Silva, and Anselmo C. Paiva "Segmentation and reconstruction of the lung in CT affected by COVID-19 using deep learning and adaptive convex hull", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208808 (10 December 2021); https://doi.org/10.1117/12.2606289

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