# Lung Field Segmentation in Chest X-rays:
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline
**Count of Failed Cases** & **Location** & **Reason/Diagnostic Relevance** \\ \hline
5 & Bottom part & Bright region probably due to pleural effusion \\ \hline
4 & Bottom part & Pleural fluid causes bright areas \\ \hline
3 & Multiple & Infiltrates due to TB—brighter regions, bone structures not sufficiently visible \\ \hline
1 & Inner part of right lung & Increased length of cardiac silhouette, probably due to pericarditis \\ \hline
1 & Left lung’s outer side brighter & Bright area likely because of infiltrate due to pneumonia \\ \hline
1 & Bottom part & Bright region, localized pleural peel \\ \hline
1 & Inner part of right lung (right hitum) & Congestive heart failure or infiltrate due to TB \\ \hline
1 & Inner part of left lung & Extended bright region due to cardiomegaly \\ \hline
2 & Top-left/top-right part of right lung & Inaccurate symmetry detection algorithm crop or glenohumeral joint area crop. Diagnosis irrelevant. \\ \hline
7 & Multiple & CXRs either normal or pathological. The failure is irrelevant to the diagnosis and is probably due to poor contrast. \\ \hline \end{tabular}
\end{table} 
Table 4: Segmentation failure scenarios. There might be multiple failure scenarios for each CXR; this is why the location column indicates the specific part of the lungs’ field where a failure is observed. This analysis is only done for CXRs with low evaluation scores. TB: tuberculosis. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Authors (Year)** & **Main Method** & **Dataset** & **Jaccard** & **Dice** & **Stepships** & **Weaknesses** \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [37]} & Two-Stream Collaborative & SRT, Montgomery & \multirow{3}{*}{-} & \multirow{3}{*}{0.973} & \multirow{3}{*}{\begin{tabular}{c} Performance classification, \\ Few training images. \\ Combined datasets for training and \\ validation. \\ \end{tabular} } & & & & \\  & segmentation stage & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2020) [58]} & CNN-based architectures & Montgomery County & \multirow{3}{*}{0.82} & \multirow{3}{*}{0.893} & Fast training, low storage & \multirow{3}{*}{\begin{tabular}{c} Contrast enhancement usefulness in \\ terms of Jaccard measurement \\ improvement depends on the \\ selected Network architecture, \\ significantly and validation \\ testing set. \\ \end{tabular} } \\  & applied on bhartized images & Montgomery County & \multirow{3}{*}{0.842} & \multirow{3}{*}{0.893} & Fast training, low storage & \multirow{3}{*}{\begin{tabular}{c} Contrast enhancement usefulness in \\ terms of Jaccard measurement \\ improvement depends on the \\ selected Network architecture, \\ significantly and validation \\ testing set. \\ \end{tabular} } \\  & applied on bhartized images & Montgomery County & \multirow{3}{*}{0.842} & \multirow{3}{*}{0.923} & Straightround unsupervised method. & \multirow{3}{*}{\begin{tabular}{c} Deepends on least sensor visibility \\ of the rib cage and a distinguishable \\ border curve. \\ \end{tabular} } \\  & & & & & \\ \hline \multirow{3}{*}{Our method} & Rib cage points-driven region & \multirow{3}{*}{\
\begin{tabular}{c} Montgomery County \\ growing \\ \end{tabular} } & \multirow{3}{*}{0.862} & \multirow{3}{*}{0.923} & Straightround unsupervised method. & \multirow{3}{*}{
\begin{tabular}{c} Deepends on least sensor visibility \\ of the rib cage and a distinguishable \\ border curve. \\ \end{tabular} } \\  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont_. The results show that this method, despite its simplicity, gives very accurate results for the vast majority of images. Furthermore, it is shown that even a very vague recognition of rib cage points can guide the overall lung area segmentation. For algorithm output, graphs and images see Appendix A.