# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## 4 Discussion  
The proposed method was based on the robust, operator-independent identification of threshold HU values that identified three regions here referred to as aerated, intermediate and consolidated, with intuitively clear functional meaning. It follows a phenomenological approach starting from the manual segmentation of the lung and then analyzing the shape of the HU histogram. Contrary to other approaches, the thresholds that define and differentiate the lung components were identified on the basis of the shape of the HU histograms, rather than second-order features and classifiers or radiologist preferences, thus maintaining a solid link with the residual lung functionality and eliminating, a priori, any operator dependence. Prior to the quantitative analysis of the images, it was verified that the image database was consistent against inter-scanner variability, although 88% of patients were scanned on one (of the three) scanner. For this purpose, calibrated CT image quality phantom acquisitions at different X-ray currents were performed on each scanner. Importantly, inter-scanner variability was not found to significantly influence the HU value of CT images, and the database can thus be considered consistent. The lung contours were initially segmented manually. Subsequently, through the implementation of an automatic procedure implemented in a workflow on the MIM software, histograms of the HU values inside the contours were extracted. The histograms obtained were analyzed by means of three different analytical methods. The maximum gradient on the data method, likely to fit better with visual consistency in lung component separation, was applied in order to quantify the three components in our population. The consolidated component was found to have the smallest volume (126 \(\pm\) 111 cc) and the intermediate and aerated components showed a volume of 914 \(\pm\) 291 cc and 793 \(\pm\) 499 cc, respectively. A number of parameters were also extracted:  
1. Ratio between the consolidated component and the aerated component (0.16 \(\pm\) 0.89);
2. Ratio between the intermediate component and the aerated component (1.09 \(\pm\) 2.56);
3. HU value corresponding to the peak of the curve for the aerated regions (\(-\)853 \(\pm\) 56 HU);
4. Width in HU of the intermediate region (754 \(\pm\) 88 HU). Figure 9: Histograms of the distributions of (**a**) position of the peak of the aerated component and (**b**) width of the intermediate component. Some considerations may be made when comparing the findings of the current work with other reported results. [PERSON] et al. (2018), for instance, reported a median value of 2900 cc for the aerated component of both lungs in a large population of patients hospitalized in a center in a neighboring city in the same period as our study; this value was significantly higher than ours (considering paired lungs, by about 1600 cc). This difference could be explained by the different segmentation methods used; in fact, [PERSON] et al. used fixed threshold values of \(-950\) to \(-700\) HU, while we adopted individual thresholds optimized for the distinction between the aerated component and the intermediate component, with a far lower th1 value compared to those found by our individually assessed method. [PERSON] et al. (2016) reported that the percentage of the lung involved in the disease varied from 6%, for non-critically ill patients, to 38%, for critically ill patients. Their definition of the lung component involved in the disease, manually segmented, seemed to include parts of what we identified as an intermediate component, and this could explain the higher mean values relative to our consolidation estimates (by about 6.4%). [PERSON] et al. (2017) reported a median volume of disease of 295 cc on the two lungs, quite consistent with our results.