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Updated: Feb 13, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
[Deep Learning-Based Automated Segmentation Algorithms of Brain and Vertebral Substructures for Radiotherapy in
Wei Wang1, Shuxian Chen1, Mawei Jiang1
1Oncology Department, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092.
Abstract:
To evaluate the application feasibility of nnU-Net and FuseNet for automatic segmentation of pediatric medulloblastoma substructures, 60 pediatric patients who received radiotherapy and grouped by 5-year age (≤5 and >5 years) were retrospectively analyzed. Brain substructures were delineated on CT-MRI fusion images, and vertebral substructures were delineated on CT images. The three convolutional neural network models of U-Net, nnU-Net and FuseNet were trained (24 cases/group) and tested/validated (6 cases/group), with 20 external cases verifying generalization. The three models and Atlas-based methods were compared using DSC; nnU-Net/FuseNet's HD95, RAVD and manual correction time were evaluated. Results showed that FuseNet performed best in brain segmentation, outperforming Atlas and U-Net in vertebral substructures for both age groups ( P=0.028/0.005 and 0.005/0.005) but not differing from nnU-Net ( P=0.107/0.236). Its DSC exceeded 0.8 for most substructures (except cerebellar anterior lobe/hippocampus in group≤5 years; hippocampus in group>5 years), with shortest correction time in both age groups. This study has demonstrated that nnU-Net can achieve good segmentation; FuseNet can improve brain segmentation accuracy via dynamic multimodal feature fusion, with the highest correction efficiency.
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