Research Article
Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network
Table 1
Quantitative comparison of colorectal tumor segmentation results.
| Methods | Performance metrics | DSC | RR | ASD (mm) |
| 3D FCNNs [15] | 0.6519 ± 0.0181 | 0.6858 ± 0.1017 | 4.2613 ± 3.1603 | 3D U-net [12] | 0.7227 ± 0.0128 | 0.7463 ± 0.0302 | 3.0173 ± 3.0133 | DenseVoxNet [16] | 0.7826 ± 0.0146 | 0.8061 ± 0.0187 | 2.7253 ± 2.9024 | 3D MSDenseNet (proposed method) | 0.8406 ± 0.0191 | 0.8513 ± 0.0201 | 2.6407 ± 2.7975 | 3D FCNNs + 3D level set [15] | 0.7591 ± 0.0169 | 0.7903 ± 0.0183 | 3.0029 ± 2.9819 | 3D U-net + 3D level set | 0.8217 ± 0.0173 | 0.8394 ± 0.0193 | 2.8815 ± 2.6901 | DenseVoxNet + 3D level set | 0.8261 ± 0.0139 | 0.8407 ± 0.0177 | 2.5249 ± 2.8004 | 3D MSDenseNet + 3D level set (proposed method) | 0.8585 ± 0.0184 | 0.8719 ± 0.0195 | 2.5401 ± 2.402 |
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