Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy - Lin, Hongyu; Zhao, Min; Zhu, Lingling; Pei, Xi; Wu, Haotian; Zhang, Lian; Li, Ying (Frontiers in Oncology, 2024)
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 nnU-Net architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation. Firstly, 130 cases of liver tumor segmentation 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 autosegmentation model by comparing with manual contours. The nnU-Net achieved an average DSC value of 0.858 86 for validation set (20 LiTS cases) and 0.8217 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.753, which increased to 0.814 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation. Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.