Table 1. Structures of Fully Convolutional Networks a.
| Type | Channel | Filter/Pooling size | Output size |
|---|---|---|---|
| c1 + bn1 + r1 | 64 | 3 × 3 | 384 × 384 |
| c2 + bn2 + r2 | 64 | 3 × 3 | 384 × 384 |
| p1 | 64 | 2 × 2 | 192 × 192 |
| c3 + bn3 + r3 | 128 | 3 × 3 | 192 × 192 |
| c4 + bn4 + r4 | 128 | 3 × 3 | 192 × 192 |
| p2 | 128 | 2 × 2 | 96 × 96 |
| c5 + bn5 + r5 | 256 | 3 × 3 | 96 × 96 |
| c6 + bn6 + r6 | 256 | 3 × 3 | 96 × 96 |
| c7 + bn7 + r7 | 256 | 3 × 3 | 96 × 96 |
| p3 | 256 | 2 × 2 | 48 × 48 |
| c8 + bn8 + r8 | 512 | 3 × 3 | 48 × 48 |
| c9 + bn9 + r9 | 512 | 3 × 3 | 48 × 48 |
| c10 + bn10 + r10 | 512 | 3 × 3 | 48 × 48 |
| p4 | 512 | 2 × 2 | 24 × 24 |
| c11 + bn11 + r11 | 512 | 3 × 3 | 24 × 24 |
| c12 + bn12 + r12 | 512 | 3 × 3 | 24 × 24 |
| c13 + bn13 + r13 | 512 | 3 × 3 | 24 × 24 |
| p5 | 512 | 2 × 2 | 12 × 12 |
| 512 | 4096 | 7 × 7 | 12 × 12 |
| fc2 + r15 | 4096 | 1 × 1 | 12 × 12 |
| fc3 | 2 | 1 × 1 | 12 × 12 |
| dc | 2 | 32 × 32 | 384 × 384 |
| softmax | 2 | - | 384 × 384 |
Abbreviations: c, convolution layer; bn, batch normalization; r, rectified linear unit; p, pooling layer; fc, fully convolution layer; dc, deconvolution layer; c + r: convolution layer followed by rectified linear unit. softmax, decision layer to get segmentation probability map.
a
Fully convolutional network (FCN) is a type of deep neural network (DNN), which forms the basis of our framework. The whole settings in the network, from the input to the output of the network, are shown from top to bottom.