## 3 Experimental Results
### Visual Evaluation  
This section shows the representative lung lobe segmentation results, and compares our method with the segmentation method based on airway or vessel anatomical information.  
As shown in Figure 6, five representative lung lobe segmentation examples were selected here. Rows 1 to 5 are case 05, case 07, case 11, case 13 and case 42 in LOLA11, respectively. Columns 1 to 4 show the pulmonary lobe segmentation 3D results and the effects of superimposing the original data on the segmentation results' transverse, coronal and sagittal planes, respectively. It can be seen from the L05 that although case 05 showed severe deformation due to scoliosis, the segmentation results using the proposed method were not affected and the boundaries of the lung lobe segmentation results were close to the real boundaries. Although case 07 was a low-dose CT image and the noise interference was serious, the segmentation results for L07 show that this method can still obtain a better segmentation effect. Pulmonary fibrosis was serious in case 11, but because of the strong noise suppression ability of the detection method, the lung lobe boundaries in L11 were close to the real pulmonary fissure and were not affected by noise. Case 42, from an emphysema patient, also achieved better segmentation results, as shown in L42. Experimental results showed that the proposed segmentation method not only achieved good segmentation performance on high-quality CT data, but also could segment the lung lobe correctly in pathological cases.  
Compared with the lung lobe segmentation algorithm proposed by [PERSON] et al. in [12], our algorithm has better performance in detecting visible pulmonary fissures because it is not affected by the  
Figure 6: Representative results of lobe segmentation from LOLA11.  
segmentation accuracy of airways and vessels. The first and second lines in Figure 7 show the representative effects of superimposing the original data on the segmentation results' coronal planes using [PERSON]'s method and ours, respectively. From left to right are the results of case 19, case 26, case 36, and case 53. For [PERSON]'s method, the areas marked with a red ellipse were the location of error segmentation. The possible reasons for these errors included two aspects: (1) the pulmonary fissure candidate areas located by the airway and vessel information were not accurate, and (2) the weak pulmonary fissures with uneven gray scale were missed.  
Besides, segmentation results for cases with moderate and low scores are shown in Figure 8. For case 27, the right middle lobe from our method and [PERSON]'s method was inaccurate due to the incomplete horizontal fissure. For case 31, case 44 and case 45, the lung segmentation results were poor because of the invisible lung boundaries caused by lung disease, which directly affected the performance of the lobe segmentation. Furthermore, as demonstrated in L45, [PERSON]'s method had the ability to construct the right middle lobe for the CT image with invisible lung fissures, while the proposed method could not deal with this situation.  
Figure 8: Comparison between our method (the second row) and [PERSON]’s method (the first row) for moderate and low score segmentation results.  
Figure 7: Comparison between present method (second line) and [PERSON]’s method (first line) when the pulmonary fissure is visible.