Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.

Autor: Rodriguez-Obregon DE; Faculty of Sciences, Universidad Autónoma de San Luis Potosí, S.L.P., Mexico., Mejia-Rodriguez AR; Faculty of Sciences, Universidad Autónoma de San Luis Potosí, S.L.P., Mexico., Cendejas-Zaragoza L; Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico.; Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico., Gutiérrez Mejía J; Tecnologico de Monterrey, School of Medicine and Health Sciences, Mexico City, Mexico., Arce-Santana ER; Faculty of Sciences, Universidad Autónoma de San Luis Potosí, S.L.P., Mexico., Charleston-Villalobos S; Universidad Autónoma Metropolitana-Iztapalapa, Mexico City, Mexico., Aljama-Corrales T; Universidad Autónoma Metropolitana-Iztapalapa, Mexico City, Mexico., Gabutti A; Department of Radiology and Imaging, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico., Santos-Díaz A; Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico.; Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Mexico.
Jazyk: angličtina
Zdroj: Biomedical signal processing and control [Biomed Signal Process Control] 2023 Aug; Vol. 85, pp. 104905. Date of Electronic Publication: 2023 Mar 22.
DOI: 10.1016/j.bspc.2023.104905
Abstrakt: Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images.
Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images.
Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p -value of 9 . 1 × 10 -4 in low-resolution and 5 . 1 × 10 -5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average.
Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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