Research Article
Hybrid Deep Feature Fusion of 2D CNN and 3D CNN for Vestibule Segmentation from CT Images
Table 1
Parameter setting of each module of 3D DenseUNet.
| Block | Feature size | Convolution layer |
| Input | | — | Convolution 1 | | conv | Pooling | | max pooling | Dense block 1 | |
| Transition layer 1 | | conv average pooling | Dense block 2 | |
| Transition layer 2 | | conv average pooling | Dense block 3 | | (
| Transition layer 3 | | conv average pooling | Dense block 4 | |
| Upsampling layer 1 | | upconv | Sum with dense block 3 | | — | Upsampling layer 2 | | upconv | Sum with dense block 2 | | — | Upsampling layer 3 | | upconv | Sum with dense block 1 | | — | Upsampling layer 4 | | upconv | Sum with convolution 1 | | — | Upsampling layer 5 | | upconv | Output | | conv |
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