## 4 Discussion
Although promising, the CNNs developed and utilized to date for lung analysis have significant limitations. CNNs are typically trained through supervised methods, meaning both their inputs (e.g., medical images) and their labels (e.g., clinical outcomes) are used to train a model. Additionally, for a well-behaved model, a large number of previously annotated outcomes are necessary for training. Many studies have been published that attempt to limit the need for a large number of training data for neural networks, but there are currently no effective methods and algorithms for it [51]. To our knowledge, the spatial presentation of excess lung fluid in the lung has yet to be defined and characterized. This study has developed a methodology to define spatial presentation outcomes for CF subjects. Additionally, the automated process eliminates the need for clinical outcomes to be defined manually and reduces the possibility of human subjectivity upon characterization. The methodology and results of this study may be used to develop a larger database of spatial presentation outcomes for further headway towards complete automation of medical image analysis for this problem using CNNs. The results of this study should be interpreted with some limitations in mind. This study made use of one 15 mm sagittal slice (i.e., a 2D image) representing lung density within the 15 mm volume in the right lung and left lung lateral to the hilar vessels for analysis. We have intentionally chosen this route to build a technique to characterize the spatial presentation of excess lung fluid while avoiding the added complexities within the lung that may skew data, such as the consideration of large blood vessels, a normal feature in lung anatomy. Although this may be a limitation currently, the methodology developed in this study may be used as a first step toward an analysis incorporating the entire lung. A possible extension would be the evaluation of all 2D sagittal slices that complete the volumetric MR map of lung density. Each 2D slice may be analyzed independently utilizing the same methodologies with only mild modifications. Because the spatial consequences of CF do not occur uniformly throughout the lung, a 3D analysis of the lungs will likely yield additional information about the disease. In this study, we have developed a methodology to quantify the spatial heterogeneity of CF disease in 2D images of the lungs. The results of this study suggest that the spatial characteristics of CF may be used in conjunction with existing measurements, such as FEV\({}_{1}\), to investigate the link between lung function and disease status. Our methods motivate the need for more comprehensive studies as to how the distribution of lung abnormalities affects disease progression, response to medicinal drugs, and overall lung health [5; 6; 52]. The extensions of this work through future studies with larger sample sizes may increase the possibility for personalized diagnoses and treatment plans. Conceptualization, A.V.S., A.N.L., R.J.T. and U.Z.G. ; methodology, A.V.S., A.N.L., R.J.T. and U.Z.G. ; software, A.V.S. and A.N.L. ; validation, A.V.S., A.N.L., R.J.T. and U.Z.G. ; formal analysis, A.V.S., A.N.L., R.J.T. and U.Z.G. ; image acquisition, R.J.T. ; writing--original draft preparation, A.V.S., A.N.L., R.J.T. and U.Z.G. ; writing--review and editing, A.V.S., A.N.L., R.J.T. and U.Z.G. ; visualization, A.V.S., A.N.L., R.J.T. and U.Z.G. ; supervision, R.J.T. and U.Z.G. ; funding acquisition, A.V.S., A.N.L., R.J.T. and U.Z.G. All authors have read and agreed to the published version of the manuscript. U.Z.G. acknowledges support from the California State University Program for Education & Research in Biotechnology New Investigator grant. A.V.S. and U.Z.G. acknowledge support from the College of Sciences at San Diego State University. A.V.S. was supported by the ACM SIGHPC Computational & Data Science Fellowship. A.N.L. was supported by the NIH IMSD Program Grant 5R25 GM058906. A.V.S. and A.N.L. acknowledge support from NSF STEM Scholar Award #1930546. R.J.T.