## 4 Discussion
The application of BSU-layers brought improved performance in terms of fine-grain segmentation, given the fact that the result of DSC measurement 2 was 7.72% higher than conventional U-net and the result of accuracy measurement 3 was 4.18% higher than conventional U-net. However, the MHD result of U-net applying BSU-layers is worse than conventional U-net. These results indicate that the results of U-net applying BSU-layers had many incorrectly segmented areas. Figure 7 shows many incorrectly segmented areas in the results of U-net applying BSU-layers and they decreased the accuracy of whole segmented areas. These incorrectly segmented areas occurred because BSU-layers preserved the detailed information which was discarded in the feature extraction process in conventional U-net. The retention of this information affected the performance of the network. Therefore, in order to fully utilize the advantages of BSU-layers, there is a need for a guiding mechanism that can discard unnecessary parts and narrow the target area into proper regions. Cascaded learning method can use the outcomes of conventional U-net to effectively guide BSU-layers  
Figure 10: Comparison between dilated U-net and BSU-net. Blue area denotes segmentation results of dilated U-net and green area denotes segmentation results of BSU-net. to focus on the proper regions. This is the reason why BSU-net, which combines cascaded learning method and BSU-layers at the same time, can achieve a high performance. Figure 7d shows the successful segmentation results of BSU-net without incorrectly segmented area. Appropriate guidance for BSU-layers improved the efficiency of the entire network. In general, cascaded learning uses the outcomes of former networks as inputs at the beginning of following networks [17; 18; 19]. However, cascaded learning applied to BSU-net puts the outcomes of conventional U-net at the back-end rather than the beginning of the following network. This is because detailed information of conventional U-net outcomes disappeared during the pooling process in the encoding part of the network. A network showed 1.67%, 4.01%, and 2.92% lower accuracy for three DSC measurements respectively when the outcomes of conventional U-net were put into the initial part of the following network. As shown in Table 3, standard deviations of cascaded U-net are highest in most accuracy measurements. Figure 9 also shows the unstable performance of cascaded U-net. For eight out of the 25 cases, cascaded U-net showed over 1% lower accuracy than conventional U-net in all eight cases; two of these showed more than 7% lower accuracy. Contrastingly, BSU-net showed lower accuracy than conventional U-net in just one case where the difference is smaller than 1%. This is because important information pertaining to the boundary areas was discarded during the feature extraction process in cascaded U-net. The loss of important information in the max-pooling process is a noticeable problem. On the other hand, BSU-net distinguished most intervertebral disc areas correctly, while unsegmented areas and over-segmented areas did not deviate much from the actual boundaries. These results also indicate that the application of BSU-layers to cascaded U-net provides stability and generality to the network. Furthermore, the use of BSU-layers enables efficient training of the network. Cascaded U-net used in our experiments has 63,912,898 trainable parameters in a total of 42 convolutional layers (3 \(\times\) 3 convolutional layers: 41 and 1 \(\times\) 1 convolutional layer: 1), while BSU-net has 53,740,674 trainable parameters, approximately 16% less than that in cascaded U-net, in a total of 79 convolutional layers (3 \(\times\) 3 convolutional layers: 35 and 1 \(\times\) 1 convolutional layer: 44).