Rodriguez-Obregon DE, Mejia-Rodriguez AR, Cendejas-Zaragoza L, Gutiérrez Mejía J, Arce-Santana ER, Charleston-Villalobos S, Aljama-Corrales T, Gabutti A, Santos-Díaz A. Semi-Supervised COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using Probabilistic Active Contour and CNN Segmentation.
Biomed Signal Process Control 2023;
85:104905. [PMID:
36993838 PMCID:
PMC10030333 DOI:
10.1016/j.bspc.2023.104905]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 03/11/2023] [Accepted: 03/18/2023] [Indexed: 03/24/2023]
Abstract
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 for 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 as 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.
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