###### Abstract
The classification between early-stage patients and progressive phases has been thoroughly investigated [15; 16; 17; 18], and all of them lead to the same conclusion: the main COVID-19 features can be difficult to detect in early stages of the disease, and their correct identification is strongly dependent on the radiologist's expertise. The role of HRCT in COVID-19 infection management is also controversial due to the radiation exposure problem. Initially, the American College of Radiology (ACR) and the Italian Society of Medical and Interventional radiology (SIRM) guidelines did not recommend HRCT in the diagnostic workup, the former referring to 2018 guidelines for acute respiratory illnesses [19; 20; 21]. With the increase of available evidence resulting from clinical trials, a prognostic role of HRCT is now emerging, increasing its value beyond the diagnosis [3; 22]. Moreover, a study from [PERSON] et al. evaluated the risk-benefit ratio of radiation exposure in COVID-19 patients, stating that HRCT is justified in patients older than 30 years [23]. The application of automated methods to support the clinicians in the analysis of a large amount of data aims to remove the subjectivity of the measurement and improve the time required for the diagnosis formulation [21; 24; 25]. Machine learning and deep learning models applied in the medical research field are becoming more popular as the basis for clinical decision support systems. Medical image segmentation plays a pivotal role in the automation of these applications since a correct segmentation of anatomical structures is a crucial step to improve the accuracy of the algorithms and to minimize possible confounders. In 2015, [PERSON] et al. [26] proposed a classification of classical image processing segmentation algorithms, where they divided them into four classes: (1) thresholding-based methods [27]; (2) region-based methods [28; 29]; (3) shape-based methods [30]; (4) neighboring anatomy [31]. Deep learning models are outperforming all these techniques [32; 33]. Deep learning applications have achieved the most significant results also in the COVID-19 literature [24; 27; 34; 35; 36], with extremely high classification performances and low execution time. The drawback of these methods is the demand for labeled data: the training of a deep learning model requires a vast amount of manual (or semi-automatic) labelled data. This is problematic because data annotation is a very time-consuming operation, dependent on the operator experience. To overcome this issue, many authors, such as [PERSON] et al. [34], proposed a different approach, combining deep learning features with standard machine learning one, proving the efficiency of this synergy. This approach could also increase the ability to explain the model, potentially leading to an improvement in the understanding of the main features of the COVID-19 disease. [PERSON] et al. highlighted the irregularity and heterogeneous intensities of the lung lesion textures as COVID-19 significant features. Concurrently, in non-COVID-19 patients, [PERSON] et al. found stronger uniformity of Hounsfield values in the chest CT within the lesions. Both these features can be automatically quantified by a machine learning algorithm, allowing stratification of the patients' severity and removing possible false positives. Other identified useful characteristics concern the geometry and shape of the lesions [8; 37]. For a more detailed stratification of patients, the Radiological Society of North America (RSNA) released a consensus statement, endorsed by the Society of Thoracic Radiology and the American College of Radiology (ACR), that classifies the CT appearance of COVID-19 pneumonia into four categories: \"typical,\" \" atypical,\" \"undetermined,\" and \"negative\" [38]. The \"typical\" pattern is characterized by the presence of round-shaped Ground-Glass Opacities (GGO), usually bilateral with a sub-pleural location on the dorsal basal segments.