# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## 4 Discussion
Their method of segmentation of the consolidated component was semi-automatic; the radiologist identified areas of the disease and a region-growing algorithm was then applied, likely resulting in the identification of individual thresholds based on maximum contrast with the tissue surrounding the consolidated component. [PERSON] et al. (2015), by applying thresholds to CT values, extracted three quantitative features corresponding to the percentage of GGO volume, semi-consolidation volume and consolidation volume. The percentages of GGO and semi-consolidation volumes were part of what we defined as intermediate component, and were therefore not directly comparable. The consolidation volume percentage obtained in this study was 5.8%, comparable to our estimate (6.4%). [PERSON] et al. (2018) found a volume percentage for the aerated component, using fixed threshold values of \(-950\) to \(-800\) HU, of 43% of the entire segmented lung. This value was in line with that found with our segmentation method, as could be expected, as their fixed threshold value was similar to the median value of our population. More generally, concerning the choice of threshold to define the aerated component, our findings suggest that for COVID-19 patients the quantity of air inflated during a deep inspiration before the CT scan is highly variable among patients, reflecting the variable residual functionality of the aerated lung. Thus, the individual choice of a \"best threshold \" seems to be a more reasonable option, able to adapt the \"aerated lung\" definition to each individual patient/lung. In addition, the relatively large variability of the peak position of the aerated component is also a clear measurement of the change of lung perfusion compared to healthy patients, typically presenting their peak at much lower values. In conclusion, this work allowed the development of a semi-automatic, operator independent segmentation method identifying the main lung components of COVID-19 patients with respiratory syndrome. Our approach intrinsically gives clear functional meaning to the different sub-volumes identified and overcame the approximation due to the choice of fixed thresholds, taking individual lung functionality carefully into account; this is especially true for th1. Very importantly, due to its simplicity and to the clear, reproducible and interpretable features that can be extracted, the suggested approach can easily be transferred from our institute. Work is currently underway to make the segmentation method fully automatic; as a matter of fact, manual segmentation is cumbersome and this is a limit of our current approach. An atlas, based on CT images of COVID-19 patients, has been developed to automate the initial identification of the lungs, and is currently being validated. Combining this atlas with the method suggested here should allow an automatic sub-segmentation of the lung components to be obtained in no more than a few minutes. In addition, the quantitative analysis carried out on the HU histograms is being applied to develop predictive models of early clinical outcome. On the other hand, atlas-based approaches for automatic segmentation of the whole lungs do not represent the only way to solve the issue; further research regarding AI/deep learning approaches may be warranted. **Author Contributions:** Conceptualization, A.M., C.F. and A.d.V. ; Data curation, A.M., A.L., M.M. and D.P. ; Formal analysis, A.M., A.L., M.M. and P.G.E. ; Investigation, C.F. and A.d.V. ; Methodology, A.M., C.F., A.L. and M.M.M. ; Software, A.M. and P.G.E. ; Supervision, C.F., F.d.C. and A.d.V. ; Writing--original draft, A.M.; Writing--review & editing, C.F., A.L., M.M., P.G.E., D.P., F.d.C. and A.d.V. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** [PERSON] is funded by an AIRC grant (IG 23015).