# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## 1 Introduction  
Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel RNA coronavirus from the same family as SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), was identified in early January 2020 as the cause of a pneumonia epidemic initially striking China, from where it spread very rapidly around the world. The World Health Organization named the syndrome coronavirus disease 2019 (COVID-19) and subsequently declared it a pandemic, given its widespread infectivity and high rate of contagion. To date, many millions of cases have been confirmed worldwide [1]. Human coronaviruses typically cause respiratory and enteric infections. SARS-CoV-2 infection mainly presents flu-like symptoms such as fever, cough, and fatigue, similar to other coronaviruses. In severe cases, the virus can cause severe interstitial pneumonia, acute respiratory distress syndrome (ARDS), and subsequent multi-organ failure, responsible for severe acute respiratory failure and high mortality rates. Chest CT plays an important role in diagnosing COVID-19 [2]. Recent studies have reported the sensitivity and specificity of CT in COVID-19 pneumonia to be 60-98% and 25-53%, respectively [3; 4]. A recent study reported positive and negative predictive values of chest CT in COVID-19 pneumonia of 92% and 42%, respectively [3]. The low negative predictive value suggests that CT may not be suitable as a screening method in COVID-19 pneumonia, at least in the early stages of the disease. CT sensitivity depends on the time elapsed after the onset of symptoms [5], and CT findings vary widely for this disease, depending on clinical severity and the time from symptom onset [6; 7]. The disease most commonly affects the lower and peripheral areas of both lungs, with a prevalent multifocal pattern [8]. The posterior areas are affected in about 80% of cases, and the disease is generally quite extensive, with all five lobes affected in a large proportion of patients [9]. Although irregular multifocal distribution is more common than diffuse disease [8], both unilateral and unifocal involvement may occur. Ground glass opacity (GGO) is defined as a hyper-attenuated area of the lung without darkening of the underlying vessels, and is typically caused by partial filling of the air spaces or interstitial thickening [10]. GGO appears to be the most common CT finding, observed in up to 98% of patients, and is usually the first manifestation [11; 12]. It may or may not be accompanied by other findings, in particular consolidation and reticulation [9]. Consolidation is defined as a hyper-attenuated area with darkening of the underlying vessels, caused by the complete filling of the alveolar air spaces [10]. Consolidations are generally irregular, while round-shaped lesions have been reported in 11-54% of patients [13], and are considered to be relatively more specific for this disease [14]. Consolidation usually appears later than GGO and peaks in days 10 to 12 after the onset of the disease [6]. Reticulation is defined as thickened interlobular septa and intralabular lines that appear as linear opacities on CT [10], and was the third most common CT feature, after GGO and consolidation, with a rate of 48.5-59% [8]. Crazy paving pattern (CPP) is defined as thickened interlobular septa or intralabular lines superimposed on GGO, resembling paving stones [12]. This finding may refer to alveolar edema and acute interstitial inflammation, also present in severe acute respiratory syndrome (SARS). It has been reported in 5-36% of patients with COVID-19 pneumonia [12]. Several recent works have evaluated the correlation between quantitative features extracted from CT and the clinical outcome of patients with COVID-19 pneumonia. Few articles have dealt with the prediction of disease severity and short-term disease progression [15; 16; 17]. Other authors have developed predictive models of diagnosis and prognosis [18; 19; 20; 21; 22].