## 4 Discussion
As seen in Figure 12C, the localization of excess lung fluid is in the center of the lung where large blood vessels are present. To the best of our ability, we aimed to choose a slice of the lung that was lateral to the hilar vessels while maintaining equidistance between the left and right lung images chosen. Subjects with narrow lungs have fewer 15 mm slices making up their total lung volume, therefore, limiting our ability to exclude the hilar vessels. The discussion of this control sample is essential, as this is the only control subject not in the generalized spatial presentation group. It is important to note that the remaining anatomy shows sparse excess fluid, which is expected for healthy control subjects. The image processing technique and metrics developed focus on segmenting and identifying excess lung fluid through comparison to healthy control subjects. This comparative approach for identifying excess fluid may be used to analyze images using other quantitative imaging techniques. For instance, CT images will differ from MR images because CT tissue density includes non-water components from lung tissue, but both imaging techniques measure the presence of fluid in the lungs. Thus, our methodology may be applied to investigate whether differences between CF and healthy control subjects can be observed in a variety of other imaging studies. Unlike many other genetic diseases, CF patients have access to therapies developed to target specific CFTR gene mutations [29]. Though not a cure, four separate FDA-approved CFTR modulator therapies have brought hope to CF patients and their families by treating the defect directly instead of treating symptoms [30; 31]. In late October 2019, the most recent CFTR modulator therapy was FDA approved. The triple combination of elexacaftor/tezacaftor/ivacaftor (Trikafta(tm), Vertex Pharmaceuticals Inc., Boston, MA, USA) has been designed to treat CF patients with one copy of the F508 del mutation benefiting 90% of the CF patient population [32; 33]. With FEV\({}_{1}\) being the primary efficacy endpoint in CFTR modulator clinical trials, there is limited information on how, and to what degree, these therapies have on regional lung structure [34; 35; 36]. While imaging studies have not been used in CFTR modulator clinical trials, there are a few observational imaging studies in patients treated with CFTR therapy. Consequently, there is limited information on the nature of how CFTR therapy alters the underlying structural implications of CF. To date, there are four computed tomography studies (CT) [37; 38; 39; 40]. These studies have reported decreases in peribronchial thickening and mucus plugging along with ventilatory improvements utilizing the Brody CT scoring system [5] in response to CFTR modulator therapy. None of these studies have developed a metric or utilized metrics which present the degree and severity of heterogeneity observed across the CF population. Through the implementation of our metrics, we can determine the scope of what remains after CFTR therapy, such as whether a specific type of heterogeneity (localized vs. diffuse disease) progresses at a different rate. In conjunction with clinical classification (FEV\({}_{1}\)), our spatial presentation groupings have the potential to be employed to guide these therapies and assess changes in disease status and response to treatment. In recent years, image analysis algorithms have significantly aided the study of lung diseases [41; 42; 43; 44]. The majority of image analysis algorithms for studying lung disease focus on tissue segmentation [45; 46; 47; 48; 49]. The field has been enhanced by innovations in the field of machine learning, such as the development of the convolutional neural network (CNN), a machine learning tool that uses imaging data to accomplish predictive tasks without the need for human interference. A study by [PERSON] et al. [50] used CNNs to detect the presence of structural abnormalities in lung tissues and classified CF CT images as bronchiectasis, atelectasis, or mucus plugging.