Computed Tomography (CT) imaging plays a pivotal role in diagnosing, characterizing, predicting outcomes, and tracking disease progression in individuals affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yet, for a consistent and dependable assessment of pulmonary irregularities, precise segmentation and quantification of both the complete lung and lung lesions (anomalies) in chest CT scans of COVID-19 patients are indispensable. Regrettably, the manual segmentation and quantification of extensive datasets can prove time-intensive and yield low levels of agreement both between different observers and within the same observer, even among experienced radiologists.
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