Virtual monochromatic image-based automatic segmentation strategy using deep learning method.
Authors
Affiliations (10)
Affiliations (10)
- School of Physics, Beihang University, Beijing 102206, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
- The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang 61000, China.
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China.
- Department of Technology, CAS Ion Medical Technology Co., Ltd., Beijing 100190, China.
- Peking University School of Physics, Beijing 100871, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China.
- State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China.
- School of Physics, Beihang University, Beijing 102206, China. Electronic address: [email protected].
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China. Electronic address: [email protected].
Abstract
The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs). The VMIs from 40 keV to 190 keV were retrospectively generated at intervals of 10 keV using the DECT of 46 patients. Images with expert delineation were used for training, validation, and testing MIAU-Net for automatic segmentation. Theperformance of MIAU-Net was compared with the existingU-Net, Attention-UNet, nnU-Net and TransFuse methods based on Dice Similarity Coefficient (DSC). Correlationanalysis was performed to evaluate and optimize the impact of different virtual energies on the accuracy of segmentation. Using MIAU-Net, average DSCs across all virtual energy levels were 93.78 %, 81.75 %, 84.46 %, 92.85 %, 94.40 %, and 84.75 % for the brain stem, optic chiasm, lens, mandible, eyes, and optic nerves, respectively, higher than the previous publications using SECT. MIAU-Net achieved the highest average DSC (88.84 %) and the lowest parameters (14.54 M) in all tested models. The results suggested that 60 keV-80 keV is the optimal VMI energy level for soft tissue delineation, while 100 keV is optimal for skeleton segmentation. This work proposed and validated a novel deep learning model for automatic segmentation based on DECT, suggesting potential advantages and OAR-specific optimal energy of using VMIs for automatic delineation.