# Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
## 3 Results and Discussion
### Clinical Assessment  
From the visual inspection, radiologists concluded that the model has a good overall performance, especially for healthy cases for which the model always set a correct segmentation. They tried to identify the physiological reasons that made the model fail and that can be taken into consideration in future work. The radiologists identified that the model tends to fail in areas of the lung that have different densities than expected.  
In areas of higher density (\"whiter\" on the CT image) than the surrounding lung, resulting from involvement by interstitial lung disease or inflammatory/infectious pathology, they are not recognized by the model as lung tissue (see first three rows in Figure 6), although they corresponded to areas of \"diseased\" lung, involved by interstitial pathology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Pattern**} & \multicolumn{2}{c}{**Dice Similarity Coefficient**} & \multirow{2}{*}{**\# CT Scans**} \\ \cline{2-3}  & **Mean \(\pm\) Standard Deviation** & \\ \hline Micronodules & \(0.9545\pm 0.1372\) & 22 \\ \hline Bronchioectasis & \(0.9377\pm 0.1845\) & 2 \\ \hline Emphysema & \(0.9442\pm 0.1748\) & 1 \\ \hline Fibrosis & \(0.9192\pm 0.1752\) & 24 \\ \hline Macronodules & \(0.9281\pm 0.2142\) & 3 \\ \hline Reticulation & \(0.9375\pm 0.1389\) & 3 \\ \hline Consolidation & \(0.9015\pm 0.2140\) & 2 \\ \hline Ground-glass & \(0.9436\pm 0.1298\) & 15 \\ \hline _Pneumocystis carinii_ pneumonia & \(0.8750\pm 0.1290\) & 2 \\ \hline Other & \(0.9255\pm 0.1731\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results obtained for the patterns included in the HUG-ILD dataset. The row “Other” refers to scans that presented more than one pattern.  
Figure 3: _Cont._  
Figure 3: Examples of HUG-ILD images for pathological cases. The patterns of these images from top to bottom are, respectively, micronodules, bronchiocentasis, emphysema, fibrosis, macronodules, reticulation with ground-glass, consolidation, ground-glass, and _pneumocystis cariniii_ pneumonia. For the last five examples, the model failed to segment part of the lung due to the pathological changes present in the image.  
Figure 4: Examples of VESSEL12 images for which the model produces good segmentation masks.  
Figure 5: Examples of CHUSJ images, the ground truth and predicted mask. The first row is an example in which the model fails to correctly segment the pulmonary base; and second and third rows are examples in which the model does not segment the high density tumor masses.  
In the case of lung neoplasms, something similar happens: the whole healthy lung is properly recognized; however, only the area of the lung mass, which is denser (\"whiter\") than the remaining lung, is not properly recognized (see last two rows in Figure 5).  
A similar situation occurs in areas of the lung that are even less dense than usual (\"blacker\" on the CT image) which may also correspond to areas of \"diseased\" lung, in this case, areas of \"air-trapping\", emphysema, etc. (see the last row in Figure 6).  
Lung pathologies present specific patterns, which correspond to changes in the density of the radiological image. Density increases for pulmonary consolidation present in pneumonia or other inflammatory/infectious processes; areas of opacification in ground-glass present innumerable causes, such as COVID, neoplasm, lung masses, and nodules. On the other hand, the density decreases with air-trapping, which is often associated with small airway disease and emphysema.  
Figure 6: Examples from HUG-ILD dataset. First three rows show cases with higher density that the model does not classify as being lung tissue. Last row shows a darker pulmonary region misclassified by the model.