Table 2.
Comparisons of segmentation performance between DFN-R and previous studies.
| Studies | Algorithm | Images | Average DSC | Patient number | Journal |
|---|---|---|---|---|---|
| Linguraru [8] | SVM | CT | 0.74 | 101 | IEEE TMI, 2012 |
| Foruzan [9] | SVM | CT | 0.82 | 35 | IJCARS, 2016 |
| Li [12] | CNN | CT | 0.80 | 30 | JCC, 2017 |
| Li [13] | CNN | CT | 0.74 | 248 | J PERS MED, 2022 |
| Christ [15] | CFCNs | DW-MRI | 0.69 | 31 | MICCAI, 2016 |
| Fabijańska [17] | U-Net | DCE-MRI | 0.48 | 9 | ICCVG, 2018 |
| Khaled [18] | DCNN | T1-weighted MRI | 0.68 | 174 | ABDOM RADIOL, 2020 |
| Zheng [19] | CNN | DCE-MRI | 0.83 | 190 | IEEE TMI, 2020 |
| Current study | DFN-R | HBP-MRI and PVP-MRI | 0.83 | 51 | — |
Notes: MRI: magnetic resonance imaging; DCNN: deep convolutional neural networks; DFN: deep fusion network; SVM: support vector machine; CNN: convolutional neural networks; CFCNs: cascaded fully convolutional neural networks; DW-MRI: diffusion-weighted MRI; DCE-MRI: dynamic contrast-enhanced MRI.