Table 4 Segmentation methods for radiation therapy (RT)
From: Practical utility of liver segmentation methods in clinical surgeries and interventions
Reference | Method | Dataset | Performance |
|---|---|---|---|
Li et al. [108] | Voxel-based Adaboost is used for liver localization. Shape and appearance models are employed to segment the liver, followed by free form deformation for refinement | MICCAI Sliver07 | Liver Dice: 0.911 ± 0.010 (CT), 0.922 ± 0.011 (CTce) Tumor burden RMSE: 0.015 |
Wu et al. [9] | Liver volume is extracted by histogram-based adaptive thresholding and morphological operations, followed by graph cuts | MICCAI Sliver07 | VOE: 7.54% RVD: 4.16% ASD: 0.95 mm RMSD: 1.94 mm MaxD: 18.48 mm Run time: 12.21 sec Tumor burden RMSE: 0.016 |
Wang et al. [109] | Adaptive mesh expansion model (AMEM) is used for liver segmentation from CT scans. A virtual deformable simplex model (DSM) is introduced to represent the mesh | MICCAI Sliver07 | Mean overlap error: 6.8% Mean volume difference: 2.7% ASSD: 1.3 mm RMSD: 2.7 mm Tumor burden RMSE: 0.016 |
Yuan et al. [110] | Hierarchical convolutional-deconvolutional neural networks (CDNN) for liver and tumor segmentation, followed tumor estimation | LiTS | Liver Dice: 0.967 Liver RMSD: 2.303 Tu mor Dice: 0.82 Tumor RMSD: 1.678 Tumor burden RMSE: 0.017 |