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Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Purpose: To improve the liver 
auto-segmentation performance of three-dimensional (3D) U-net by replacing 
the conventional up-sampling convolution layers with the Pixel De-convolutional 
Network (PDN) that considers spatial features. Methods: The U-net was 
originally developed to segment neuronal structure with outstanding performance 
but suffered serious artifacts from indirectly unrelated adjacent pixels in its 
up-sampling layers. The hypothesis of this study was that the segmentation 
quality of the liver could be 
improved with PDN in which the up-sampling layer was replaced by a pixel 
de-convolution layer (PDL). Seventy-eight plans of abdominal cancer patients were 
anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D 
U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen 
plans were used to test the performance. The similarity Dice and Average 
Hausdorff Distance (AHD) were calculated and compared between these two 
networks. Results: The computation time for 62 training cases and 200 
training epochs was about 30 minutes for both networks. The segmentation 
performance was evaluated using the remaining 16 cases. For the Dice score, the 
mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and 
U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 
0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% 
and 51.5% of mean and standard deviation for the PDN. Conclusion: The 
PDN has outperformed the U-Net on liver auto-segmentation. The predicted 
contours of PDN are more conformal and smoother when compared with the U-Net.