Table 2 show the evaluation results of 33 cubes obtained by the proposed method and seven other methods.
12–15,18–20 To further evaluate the performance of the enhanced algorithm and our framework, we also added the contrast experiments using input 1 as the input to other methods. As it can be seen from
Table 2, our proposed method performs best with DSC of 70.73%, and a significant difference (
P < 0.0001) was observed for DSC between our proposed method and other methods, which indicates that our proposed method exhibits the state-of-the-art performance and robustness. Generally speaking, the traditional methods
12,13 relied on handcrafted features to a great extent and were not robust enough. The performance of the grow-cut-based method
12 was the worst among all the methods. The component tree method
13 performed well. However, the processes of this method
13 were complicated and the results existed more oversegmentation. Yu et al.
14 treat the segmentation task as a patch-based classification problem. Although this method reached the best performance with Precision, it took a long time to predict a new B-scan, which was not practical for real application. The residual block of ResUNet and the deformable convolution block of DUNet might make feature extraction of complicated HRF more difficult. Although the performance of U-Net++ was desirable and similar to ours, the number of the parameters of the U-Net++ was huge, and the test procedure to obtain more accurate results was complicated. Also, we can see from
Table 2 that the results and robustness of ResUNet, DUNet, and UNet++ were significantly improved by our input, which proved the effectiveness of our input. However, our proposed method still performed the best, which proved the effectiveness of our network at the same time. The FCN was not a good choice for medical imaging segmentation. The results did not improve even though it used our input as input. In summary, our proposed method is simple to train and test while it reaches the best overall performance comparing to those listed methods.