TITLE:
Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network
AUTHORS:
Huan Yao, Jenghwa Chang
KEYWORDS:
Liver Auto-Segmentation, Deep-Learning, U-Net, Pixel-Deconvolutional Network
JOURNAL NAME:
International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,
Vol.10 No.2,
May
28,
2021
ABSTRACT: 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.