## 3 Experimental Results
### Quantitative Evaluation  
The quantitative evaluation results came from the online evaluation system of the competition website. The 55 LOLA11 cases' segmentation results were uploaded to the competition website, and the background evaluation system executed the evaluation procedure and fed back the evaluation results. Specifically, according to Formula (4), each lung lobe's coincidence degree in each data object was calculated, and then the coincidence degree mean value from each type of lung lobe was calculated. The overall score was the average value of the five lung lobes' mean coincidence degrees. Furthermore, we used six other quantitative evaluation indicators based on the mean statistics including variance (SD), minimum (Min), quarter (Q1), median (Median), three-quarter (Q3) and maximum (Max).  
Table 1 presents the total quantitative evaluation results. The proposed method's overall score was 0.84. The highest and lowest scores of single class assessment were from the left upper lobe (0.93) and right middle lobe (0.65), respectively. The variance of the right middle lobe was 0.4, which indicated that the data difference had a great influence on the segmentation performance and the reason was that there were many incomplete right lung horizontal fissures. For a more comprehensive understanding, including the effect of each case's segmentation in the LOLA11 dataset, Figure 9 describes the specific coincidence degree score of each case.  
As shown in Figure 9, many wrong segmentation results exist in the right middle lobes and there is still a lot of room for improvement. The reason for the poor performance was missing the detection of the horizontal fissure because it was difficult to image the horizontal fissures in the CT images when the horizontal fissures were nearly parallel to the CT scanning plane.  
To further verify the performance of the proposed method, we compared the average score from each lung lobe between our method and two representative AI-based methods (a sequence of convolutional neural networks for marginal learning [20] and progressive dense V-Network [4]). The quantitative evaluation results on each lung lobe are shown in Table 2 (LobeNet_V2 is the name of the net in [20] given by authors for LOLA11). It indicates that our proposed method achieved a similar performance for most lung lobes except the right middle lobe, and there is even better segmentation performance on the left lung compared with V-net. Moreover, the proposed algorithm does not need a large number of labeled data for training and has advantages in operating efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **SD** & **Min** & **Q1** & **Median** & **Q3** & **Max** \\ \hline Left lower lobe & 0.89 & 0.23 & 0.0 & 0.95 & 0.97 & 0.98 & 0.99 \\ Left upper lobe & 0.93 & 0.18 & 0.02 & 0.96 & 0.99 & 0.99 & 1 \\ Right upper lobe & 0.84 & 0.27 & 0.0 & 0.8 & 0.96 & 0.99 & 1 \\ Right middle lobe & 0.65 & 0.4 & 0.0 & 0.06 & 0.82 & 0.96 & 1 \\ Right lower lobe & 0.88 & 0.24 & 0.0 & 0.93 & 0.97 & 0.98 & 0.99 \\ \hline Total score & 0.84 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall quantitative assessment of lobe segmentation on LOLA11 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LobeNet\_V2** & **V-Net** & **The Proposed Method** \\ \hline Left lower lobe & 0.91 & 0.88 & 0.89 \\ Left upper lobe & 0.95 & 0.92 & 0.93 \\ Right lower lobe & 0.96 & 0.92 & 0.88 \\ Right middle lobe & 0.87 & 0.76 & 0.65 \\ Right upper lobe & 0.94 & 0.91 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between our proposed method and AI-based methods.