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Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy
- Source :
- Frontiers in Oncology, Vol 14 (2024)
- Publication Year :
- 2024
- Publisher :
- Frontiers Media S.A., 2024.
-
Abstract
- PurposeAddressing 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.MethodsFirstly, 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. ResultsThe 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
Details
- Language :
- English
- ISSN :
- 2234943X
- Volume :
- 14
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Oncology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7381032dfaf34aee9bbde7bf5ffd1381
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fonc.2024.1423774