## 3 Results  
As shown in Table 1, both dilated U-net and BSU-net showed better results than conventional U-net in all DSC measurements. Furthermore, BSU-net showed better results than dilated U-net. As observed from these common trends, application of cascaded learning, BSU-pooling, and residual learning improved segmentation performance. In DSC measurement 1 (whole area segmentation), dilated U-net showed 2.02% higher DSC than conventional U-net and BSU-net showed a 3.00% higher DSC than conventional U-net. In DSC measurement 2 (boundary segmentation, thickness = 1 pixel), dilated U-net showed 8.29% higher DSC than conventional U-net and BSU-net showed 10.45% higher DSC than conventional U-net. In DSC measurement 3 (boundary segmentation, thickness = 2 pixels), dilated U-net showed 5.66% higher DSC than conventional U-net and BSU-net showed 7.34% higher DSC than conventional U-net. MHD results for three different networks showed similar trends (Table 2). Dilated U-net showed 0.03 mm lower MHD than conventional U-net and BSU-net showed 0.08 mm lower MHD than conventional U-net. Figure 5 compares the distributions of results according to the three DSC measurements and MHD measurement. In three DSC measurements, dilated U-net and BSU-net showed significant improvement in performance over conventional U-net. In DSC measurement 1, dilated U-net showed significantly increased DSC compared to conventional U-net (\(p<0.01\)) and BSU-net showed significantly higher DSC compared to conventional U-net (\(p<0.001\)). convolutional layers and pooling layers are used instead of BSU-layers. Cascaded U-net showed higher DSC and smaller MHD compared to conventional U-net. The application of each component improved the segmentation performance in most cases. Figures 7-9 show the results of the five different networks in Table 3. Figure 7b-d shows segmentation results of conventional U-net, U-net applying BSU-layers, and BSU-net, respectively. U-net applying BSU-layers segmented more delicately than conventional U-net, but there are some incorrectly segmented areas. On the other hand, the results of BSU-net have detailed boundaries and no incorrectly segmented area. Figure 8b-d shows segmentation results of conventional U-net, cascaded U-net, and BSU-net, respectively. The white pixels represent estimated boundary pixels that are perfectly matched with true boundary labels. It is easily noticeable that cascaded U-net found a higher number of true boundary pixels than conventional U-net, and BSU-net detected the most among the three different networks. The enlarged views at the bottom of Figure 8 clearly show the results from each and demonstrate the improved performance of BSU-net. Figure 9b-d also shows segmentation results of conventional U-net, cascaded U-net, and BSU-net, respectively. In this case, cascaded U-net did not properly segment intervertebral disc, and its results are worse than those of conventional U-net. In some cases of cascaded U-net, it segmented intervertebral discs smaller than their actual size. On the other hand, BSU-net showed successful performance in these cases. Standard deviations in Table 3 shows the stability of BSU-net. Standard deviations of BSU-net are the lowest in most accuracy measurements while those of cascaded U-net are the highest in most accuracy measurements.