%0 Journal Article
%T Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.
%A Lin H
%A Zhao M
%A Zhu L
%A Pei X
%A Wu H
%A Zhang L
%A Li Y
%J Front Oncol
%V 14
%N 0
%D 2024
%M 38966060
%F 5.738
%R 10.3389/fonc.2024.1423774
%X <B>UNASSIGNED: </B>Addressing the challenges of unclear tumor boundaries and the confusion between cysts and tumors in liver tumor segmentation, this study aims to develop an auto-segmentation method utilizing Gaussian filter with the nnUNet architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation.<BR><B>UNASSIGNED: </B>Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours.<BR><B>UNASSIGNED: </B>The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation.<BR><B>UNASSIGNED: </B>Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.